Skip to main content
Erschienen in: Artificial Intelligence Review 2/2021

06.08.2020

360 degree view of cross-domain opinion classification: a survey

verfasst von: Rahul Kumar Singh, Manoj Kumar Sachan, R. B. Patel

Erschienen in: Artificial Intelligence Review | Ausgabe 2/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In the field of natural language processing and text mining, sentiment analysis (SA) has received huge attention from various researchers’ across the globe. By the prevalence of Web 2.0, user’s became more vigilant to share, promote and express themselves along with any issues or challenges that are being encountered on daily activities through the Internet (social media, micro-blogs, e-commerce, etc.) Expression and opinion are a complex sequence of acts that convey a huge volume of data that pose a challenge for computational researchers to decode. Over the period of time, researchers from various segments of public and private sectors are involved in the exploration of SA with an aim to understand the behavioral perspective of various stakeholders in society. Though the efforts to positively construct SA are successful, challenges still prevail for efficiency. This article presents an organized survey of SA (also known as opinion mining) along with methodologies or algorithms. The survey classifies SA into categories based on levels, tasks, and sub-task along with various techniques used for performing them. The survey explicitly focuses on different directions in which the research was explored in the area of cross-domain opinion classification. The article is concluded with an objective to present an exclusive and exhaustive analysis in the area of opinion mining containing approaches, datasets, languages, and applications used. The observations made are expected to support researches to get a greater understanding on emerging trends and state-of-the-art methods to be applied for future exploration.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Fußnoten
Literatur
Zurück zum Zitat Abbasi A, France S, Zhang Z, Chen H (2011) Selecting attributes for sentiment classification using feature relation networks. IEEE Trans Knowl Data Eng 23(3):447–462CrossRef Abbasi A, France S, Zhang Z, Chen H (2011) Selecting attributes for sentiment classification using feature relation networks. IEEE Trans Knowl Data Eng 23(3):447–462CrossRef
Zurück zum Zitat Abdelwahab O, Elmaghraby AS (2018) Deep learning based vs markov chain based text generation for cross domain adaptation for sentiment classification. In: Proceedings of the IEEE international conference on information reuse and integration (IRI), pp 252–255. https://doi.org/10.1109/iri.2018.00046 Abdelwahab O, Elmaghraby AS (2018) Deep learning based vs markov chain based text generation for cross domain adaptation for sentiment classification. In: Proceedings of the IEEE international conference on information reuse and integration (IRI), pp 252–255. https://​doi.​org/​10.​1109/​iri.​2018.​00046
Zurück zum Zitat Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of Twitter data. In: Proceedings of the workshop on languages in social media, pp 30–38 Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of Twitter data. In: Proceedings of the workshop on languages in social media, pp 30–38
Zurück zum Zitat Algur SP, Patil AP, Hiremath PS, Shivashankar S (2010) Conceptual level similarity measure based review spam detection. In: Proceedings of the IEEE international conference on signal and image processing (ICSIP), pp 416–423 Algur SP, Patil AP, Hiremath PS, Shivashankar S (2010) Conceptual level similarity measure based review spam detection. In: Proceedings of the IEEE international conference on signal and image processing (ICSIP), pp 416–423
Zurück zum Zitat Baccianella S, Esuli A, Sebastiani F (2008) SENTIWORNET 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the seventh conference on international language resources and evaluation, pp 2200–2204 Baccianella S, Esuli A, Sebastiani F (2008) SENTIWORNET 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the seventh conference on international language resources and evaluation, pp 2200–2204
Zurück zum Zitat Balahur A, Hermida JM, Montoyo A (2012a) Building and exploiting EmotiNet: a knowledge base for emotion detection based on the appraisal theory model. IEEE Trans Affect Comput 3(1):88–101CrossRef Balahur A, Hermida JM, Montoyo A (2012a) Building and exploiting EmotiNet: a knowledge base for emotion detection based on the appraisal theory model. IEEE Trans Affect Comput 3(1):88–101CrossRef
Zurück zum Zitat Banea C, Mihalcea R, Wiebe J, (2008) Multilingual subjectivity analysis using machine translation. In: Proceedings of the empirical methods in natural language processing. Association for Computational Linguistics, pp 127–135 Banea C, Mihalcea R, Wiebe J, (2008) Multilingual subjectivity analysis using machine translation. In: Proceedings of the empirical methods in natural language processing. Association for Computational Linguistics, pp 127–135
Zurück zum Zitat Banerjee S, Chua AYK (2014) Applauses in hotel reviews: genuine or deceptive?. In: Proceedings of the science and information conference, pp 938–942 Banerjee S, Chua AYK (2014) Applauses in hotel reviews: genuine or deceptive?. In: Proceedings of the science and information conference, pp 938–942
Zurück zum Zitat Benamara F, Cesarano C, Picariello A, Reforgiato D, Subrahmanian V (2007) Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: Proceedings of the international conference on weblogs and social media (ICWSM 2007), pp 203–206 Benamara F, Cesarano C, Picariello A, Reforgiato D, Subrahmanian V (2007) Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: Proceedings of the international conference on weblogs and social media (ICWSM 2007), pp 203–206
Zurück zum Zitat Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media lnc, NewtonMATH Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media lnc, NewtonMATH
Zurück zum Zitat Bisio F, Gastaldo P, Peretti C, Zunino R, Cambria E (2013) Data intensive review mining for sentiment classification across heterogeneous domains. In: Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining, pp 1061–1067 Bisio F, Gastaldo P, Peretti C, Zunino R, Cambria E (2013) Data intensive review mining for sentiment classification across heterogeneous domains. In: Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining, pp 1061–1067
Zurück zum Zitat Blitzer J, Mcdonald R, Pereira F (2006) Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 conference on empirical methods in natural language processing, pp 120–128 Blitzer J, Mcdonald R, Pereira F (2006) Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 conference on empirical methods in natural language processing, pp 120–128
Zurück zum Zitat Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 440–447 Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 440–447
Zurück zum Zitat Bollegala D, Mu T (2016) Cross-domain sentiment classification using sentiment sensitive embeddings. IEEE Trans Knowl Data Eng 28(2):398–410CrossRef Bollegala D, Mu T (2016) Cross-domain sentiment classification using sentiment sensitive embeddings. IEEE Trans Knowl Data Eng 28(2):398–410CrossRef
Zurück zum Zitat Bollegala D, Weir D, Carroll J (2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans Knowl Data Eng 25(8):1719–1731CrossRef Bollegala D, Weir D, Carroll J (2013) Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Trans Knowl Data Eng 25(8):1719–1731CrossRef
Zurück zum Zitat Bosco C, Patti V, Bolioli A (2015) Developing corpora for sentiment analysis: the case of irony and senti-TUT. In: Proceedings of the international joint conference on artificial intelligence, pp 4158–4162 Bosco C, Patti V, Bolioli A (2015) Developing corpora for sentiment analysis: the case of irony and senti-TUT. In: Proceedings of the international joint conference on artificial intelligence, pp 4158–4162
Zurück zum Zitat Bravo-marquez F, Mendoza M, Poblete B (2014) Meta-level sentiment models for big social data analysis. Knowl-Based Syst 69:86–99CrossRef Bravo-marquez F, Mendoza M, Poblete B (2014) Meta-level sentiment models for big social data analysis. Knowl-Based Syst 69:86–99CrossRef
Zurück zum Zitat Brody S, Elhadad N (2010) An unsupervised aspect-sentiment model for online reviews. In: Proceedings of the human language technologies: the 2010 annual conference of the North American chapter of the Association for Computational Linguistics, pp 804–812 Brody S, Elhadad N (2010) An unsupervised aspect-sentiment model for online reviews. In: Proceedings of the human language technologies: the 2010 annual conference of the North American chapter of the Association for Computational Linguistics, pp 804–812
Zurück zum Zitat Cambria E, Speer R, Havasi C, Hussain A (2010) SenticNet: a publicly available semantic resource for opinion mining. In: Proceedings of the AAAI fall symposium: common-sense knowledge, pp 14–18 Cambria E, Speer R, Havasi C, Hussain A (2010) SenticNet: a publicly available semantic resource for opinion mining. In: Proceedings of the AAAI fall symposium: common-sense knowledge, pp 14–18
Zurück zum Zitat Cambria E, Havasi C, Hussain A (2012) SenticNet 2: a semantic and affective resource for opinion mining and sentiment analysis. In: Proceedings of the twenty-fifth international florida artificial intelligence research society conference, pp 202–207 Cambria E, Havasi C, Hussain A (2012) SenticNet 2: a semantic and affective resource for opinion mining and sentiment analysis. In: Proceedings of the twenty-fifth international florida artificial intelligence research society conference, pp 202–207
Zurück zum Zitat Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15–21CrossRef Cambria E, Schuller B, Xia Y, Havasi C (2013) New avenues in opinion mining and sentiment analysis. IEEE Intell Syst 28(2):15–21CrossRef
Zurück zum Zitat Cambria E, Olsher D, Rajagopal D (2014) SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence, pp 1515–1521 Cambria E, Olsher D, Rajagopal D (2014) SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence, pp 1515–1521
Zurück zum Zitat Cambria E, Poria S, Bajpai R, Schuller B (2016) SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: Proceedings of the 26th international conference on computational linguistics (COLING 2016), pp 2666–2677 Cambria E, Poria S, Bajpai R, Schuller B (2016) SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: Proceedings of the 26th international conference on computational linguistics (COLING 2016), pp 2666–2677
Zurück zum Zitat Cambria E, Poria S, Hazarika D, Kwok K (2018) SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Proceedings of the 32nd AAAI conference on artificial intelligence, pp 1795–1802 Cambria E, Poria S, Hazarika D, Kwok K (2018) SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Proceedings of the 32nd AAAI conference on artificial intelligence, pp 1795–1802
Zurück zum Zitat Carenini G, Ng R, Pauls A (2006) Multi-document summarization of evaluative text. In: Proceedings of the 11th conference of the european chapter of the Association for Computational Linguistics, pp 305–312 Carenini G, Ng R, Pauls A (2006) Multi-document summarization of evaluative text. In: Proceedings of the 11th conference of the european chapter of the Association for Computational Linguistics, pp 305–312
Zurück zum Zitat Che W, Li Z, Liu T (2010) LTP: a Chinese language technology platform. In: Proceedings of the 23rd international conference on computational linguistics: demonstrations, pp 13–16 Che W, Li Z, Liu T (2010) LTP: a Chinese language technology platform. In: Proceedings of the 23rd international conference on computational linguistics: demonstrations, pp 13–16
Zurück zum Zitat Chen W, Lin S, Huang S, Chung Y, Chen K (2010) E-HowNet and automatic construction of a lexical ontology. In: Proceedings of the 23rd international conference on computational linguistics: demonstrations, pp 45–48 Chen W, Lin S, Huang S, Chung Y, Chen K (2010) E-HowNet and automatic construction of a lexical ontology. In: Proceedings of the 23rd international conference on computational linguistics: demonstrations, pp 45–48
Zurück zum Zitat Cruz FL, Troyano JA, Enríquez F, Ortega FJ, Vallejo CG (2010) A knowledge-rich approach to feature-based opinion extraction from product reviews. In: Proceedings of the 2nd international workshop on Search and mining user-generated contents, pp 13–20 Cruz FL, Troyano JA, Enríquez F, Ortega FJ, Vallejo CG (2010) A knowledge-rich approach to feature-based opinion extraction from product reviews. In: Proceedings of the 2nd international workshop on Search and mining user-generated contents, pp 13–20
Zurück zum Zitat Dang Y, Zhang Y, Chen H (2010) A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell Syst 25(4):46–53CrossRef Dang Y, Zhang Y, Chen H (2010) A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell Syst 25(4):46–53CrossRef
Zurück zum Zitat Dasgupta S, Ng V (2009) Mine the easy, classify the hard : a semi-supervised approach to automatic sentiment classification. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP, pp 701–709 Dasgupta S, Ng V (2009) Mine the easy, classify the hard : a semi-supervised approach to automatic sentiment classification. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP, pp 701–709
Zurück zum Zitat Demirtas E (2013) Cross-lingual sentiment analysis with machine translation, utility of training corpora and sentiment lexica. Master dissertation, University of Technology Demirtas E (2013) Cross-lingual sentiment analysis with machine translation, utility of training corpora and sentiment lexica. Master dissertation, University of Technology
Zurück zum Zitat Derczynski L, Ritter A, Clark S, Bontcheva K (2013) Twitter part-of-speech tagging for all: overcoming sparse and noisy data. In: Proceedings of the international conference recent advances in natural language processing, pp 198–206 Derczynski L, Ritter A, Clark S, Bontcheva K (2013) Twitter part-of-speech tagging for all: overcoming sparse and noisy data. In: Proceedings of the international conference recent advances in natural language processing, pp 198–206
Zurück zum Zitat Ding X, Liu B, Zhang L (2009) Entity discovery and assignment for opinion mining applications. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1125–1134 Ding X, Liu B, Zhang L (2009) Entity discovery and assignment for opinion mining applications. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1125–1134
Zurück zum Zitat Duh K, Fujino A, Nagata M (2011) Is machine translation ripe for cross-lingual sentiment classification ? In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: short papers, pp 429–433 Duh K, Fujino A, Nagata M (2011) Is machine translation ripe for cross-lingual sentiment classification ? In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: short papers, pp 429–433
Zurück zum Zitat Farra N, Challita E, Assi RA, Hajj H (2010) Sentence-level and document-level sentiment mining for Arabic texts. In: Proceedings of the IEEE international conference on data mining workshops sentence-level (IEEE Computer Society), pp 1114–1119. https://doi.org/10.1109/ICDMW.2010.95 Farra N, Challita E, Assi RA, Hajj H (2010) Sentence-level and document-level sentiment mining for Arabic texts. In: Proceedings of the IEEE international conference on data mining workshops sentence-level (IEEE Computer Society), pp 1114–1119. https://​doi.​org/​10.​1109/​ICDMW.​2010.​95
Zurück zum Zitat Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56(4):82–89CrossRef Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56(4):82–89CrossRef
Zurück zum Zitat García-moya L, Anaya-sánchez H, Berlanga-llavori R (2013) Retrieving product features and opinions from customer reviews. IEEE Intell Syst 3:19–27CrossRef García-moya L, Anaya-sánchez H, Berlanga-llavori R (2013) Retrieving product features and opinions from customer reviews. IEEE Intell Syst 3:19–27CrossRef
Zurück zum Zitat Gerani S, Mehdad Y, Carenini G, Ng RT, Nejat B (2014) Abstractive summarization of product reviews using discourse structure. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1602–1613 Gerani S, Mehdad Y, Carenini G, Ng RT, Nejat B (2014) Abstractive summarization of product reviews using discourse structure. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1602–1613
Zurück zum Zitat Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512CrossRef Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23(10):1498–1512CrossRef
Zurück zum Zitat Gimpel K et al (2011) Part-of-speech tagging for Twitter: annotation, features, and experiments. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: short papers, pp 42–47 Gimpel K et al (2011) Part-of-speech tagging for Twitter: annotation, features, and experiments. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: short papers, pp 42–47
Zurück zum Zitat Gindl S, Weichselbraun A, Scharl A (2013) Rule-based opinion target and aspect extraction to acquire affective knowledge. In: Proceedings of the 22nd international conference on World Wide Web (IW3C2), pp 557–563 Gindl S, Weichselbraun A, Scharl A (2013) Rule-based opinion target and aspect extraction to acquire affective knowledge. In: Proceedings of the 22nd international conference on World Wide Web (IW3C2), pp 557–563
Zurück zum Zitat Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning, pp 513–520 Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning, pp 513–520
Zurück zum Zitat Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N project report, Stanford University 1(12), pp 1–6 Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N project report, Stanford University 1(12), pp 1–6
Zurück zum Zitat Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud 43:907–928CrossRef Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud 43:907–928CrossRef
Zurück zum Zitat Gui L, Xu R, Lu Q, Xu J, Xu J, Liu B, Wang X (2014) Cross-lingual opinion analysis via negative transfer detection. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics (short papers), pp 860–865 Gui L, Xu R, Lu Q, Xu J, Xu J, Liu B, Wang X (2014) Cross-lingual opinion analysis via negative transfer detection. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics (short papers), pp 860–865
Zurück zum Zitat Hai Z, Chang K, Kim J, Yang CC (2014) Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans Knowl Data Eng 26(3):623–634CrossRef Hai Z, Chang K, Kim J, Yang CC (2014) Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans Knowl Data Eng 26(3):623–634CrossRef
Zurück zum Zitat Hassan A, Radev D (2010) Identifying text polarity using random walks. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics, pp 395–403 Hassan A, Radev D (2010) Identifying text polarity using random walks. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics, pp 395–403
Zurück zum Zitat He Y, Lin C, Alani H (2011) Automatically extracting polarity-bearing topics for cross-domain sentiment classification conference item. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies, pp 123–131 He Y, Lin C, Alani H (2011) Automatically extracting polarity-bearing topics for cross-domain sentiment classification conference item. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies, pp 123–131
Zurück zum Zitat Hiroshi K, Tetsuya N, Hideo W (2004) Deeper sentiment analysis using machine translation technology. In: Proceedings of the 20th international conference on computational linguistics (COLING’04), pp 494–500 Hiroshi K, Tetsuya N, Hideo W (2004) Deeper sentiment analysis using machine translation technology. In: Proceedings of the 20th international conference on computational linguistics (COLING’04), pp 494–500
Zurück zum Zitat Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 168–177 Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 168–177
Zurück zum Zitat Hu Y, Chen Y, Chou H (2017) Opinion mining from online hotel reviews—a text summarization approach. Inf Process Manag 53:436–449CrossRef Hu Y, Chen Y, Chou H (2017) Opinion mining from online hotel reviews—a text summarization approach. Inf Process Manag 53:436–449CrossRef
Zurück zum Zitat Hung C, Lin H (2013) Using objective words in SentiWordNet to mouth sentiment classification. IEEE Intell Syst 2:47–54CrossRef Hung C, Lin H (2013) Using objective words in SentiWordNet to mouth sentiment classification. IEEE Intell Syst 2:47–54CrossRef
Zurück zum Zitat Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent Twitter sentiment classification. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics, pp 151–160 Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent Twitter sentiment classification. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics, pp 151–160
Zurück zum Zitat Jimenez SM, Martin-valdivia MT, Molina-gonzalez MD, Urena-Lopez LA (2016) Domain adaptation of polarity lexicon combining term frequency and bootstrapping. In: Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 137–146 Jimenez SM, Martin-valdivia MT, Molina-gonzalez MD, Urena-Lopez LA (2016) Domain adaptation of polarity lexicon combining term frequency and bootstrapping. In: Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 137–146
Zurück zum Zitat Jindal N, Liu B (2008) Opinion spam and analysis. In: Proceedings of the 3rd international conference on web search and data mining, pp 219–230 Jindal N, Liu B (2008) Opinion spam and analysis. In: Proceedings of the 3rd international conference on web search and data mining, pp 219–230
Zurück zum Zitat Jo Y, Oh A (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 815–824 Jo Y, Oh A (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 815–824
Zurück zum Zitat Kanayama H, Nasukawa T (2014) Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP 2006) Association for Computational Linguistics, pp 355–363. https://doi.org/10.3115/1610075.1610125 Kanayama H, Nasukawa T (2014) Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP 2006) Association for Computational Linguistics, pp 355–363. https://​doi.​org/​10.​3115/​1610075.​1610125
Zurück zum Zitat Kennedy A, Inkpen D (2006) Sentiment classification of movie reviews using contextual valence shifters. Comput Intell 22:100–125MathSciNetCrossRef Kennedy A, Inkpen D (2006) Sentiment classification of movie reviews using contextual valence shifters. Comput Intell 22:100–125MathSciNetCrossRef
Zurück zum Zitat Kim S, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on computational linguistics, pp 1367–1373 Kim S, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on computational linguistics, pp 1367–1373
Zurück zum Zitat Kim S, Zhang J, Chen Z, Oh A, Liu S (2013) A hierarchical aspect-sentiment model for online reviews. In: Proceedings of the twenty-seventh AAAI conference on artificial intelligence, pp 526–533 Kim S, Zhang J, Chen Z, Oh A, Liu S (2013) A hierarchical aspect-sentiment model for online reviews. In: Proceedings of the twenty-seventh AAAI conference on artificial intelligence, pp 526–533
Zurück zum Zitat Kong L, Schneider N, Swayamdipta S, Bhatia A, Dyer C, Smith NA (2014) A dependency parser for Tweets. In: Proceedings of the conference on empirical methods in natural language processing, pp 1001–1012 Kong L, Schneider N, Swayamdipta S, Bhatia A, Dyer C, Smith NA (2014) A dependency parser for Tweets. In: Proceedings of the conference on empirical methods in natural language processing, pp 1001–1012
Zurück zum Zitat Kouloumpis E, Wilson T, Moore J (2011) Twitter sentiment analysis: the good the bad and the OMG !. In: Proceedings of the fifth international AAAI conference on weblogs and social media, pp 538–541 Kouloumpis E, Wilson T, Moore J (2011) Twitter sentiment analysis: the good the bad and the OMG !. In: Proceedings of the fifth international AAAI conference on weblogs and social media, pp 538–541
Zurück zum Zitat Lambert P (2015) Aspect-level cross-lingual sentiment classification with constrained SMT. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing, pp 781–787 Lambert P (2015) Aspect-level cross-lingual sentiment classification with constrained SMT. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing, pp 781–787
Zurück zum Zitat Lazaridou A, Titov I, Sporleder C (2013) A Bayesian model for joint unsupervised induction of sentiment, aspect and discourse representations. In: Proceedings of the 51st annual meeting of the Association for Computational Linguistics, pp 1630–1639 Lazaridou A, Titov I, Sporleder C (2013) A Bayesian model for joint unsupervised induction of sentiment, aspect and discourse representations. In: Proceedings of the 51st annual meeting of the Association for Computational Linguistics, pp 1630–1639
Zurück zum Zitat Lerman K, Blair-goldensohn S, Mcdonald R (2009) Sentiment summarization: evaluating and learning user preferences. In: Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics, pp 514–522 Lerman K, Blair-goldensohn S, Mcdonald R (2009) Sentiment summarization: evaluating and learning user preferences. In: Proceedings of the 12th conference of the European chapter of the Association for Computational Linguistics, pp 514–522
Zurück zum Zitat Li F, Huang M, Zhu X (2007) Sentiment analysis with global topics and local dependency. In: Proceedings of the twenty-fourth AAAI conference on artificial intelligence, pp 1371–1376 Li F, Huang M, Zhu X (2007) Sentiment analysis with global topics and local dependency. In: Proceedings of the twenty-fourth AAAI conference on artificial intelligence, pp 1371–1376
Zurück zum Zitat Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. In: Proceedings of the twenty-second international joint conference on artificial intelligence, pp 2488–2493 Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. In: Proceedings of the twenty-second international joint conference on artificial intelligence, pp 2488–2493
Zurück zum Zitat Li S, Xue Y, Wang Z, Zhou G (2013) Active learning for cross-domain sentiment classification. In: Proceedings of the twenty-third international joint conference on artificial intelligence active, pp 2127–2133 Li S, Xue Y, Wang Z, Zhou G (2013) Active learning for cross-domain sentiment classification. In: Proceedings of the twenty-third international joint conference on artificial intelligence active, pp 2127–2133
Zurück zum Zitat Li H, Chen Z, Mukherjee A, Liu B, Shao J (2015) Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In: Proceedings of the ninth international association for the advancement of artificial intelligence conference on web and social media analyzing, pp 634–637 Li H, Chen Z, Mukherjee A, Liu B, Shao J (2015) Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In: Proceedings of the ninth international association for the advancement of artificial intelligence conference on web and social media analyzing, pp 634–637
Zurück zum Zitat Lin C, He Y (2009) Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on information and knowledge management, pp 375–384 Lin C, He Y (2009) Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on information and knowledge management, pp 375–384
Zurück zum Zitat Lin C, He Y, Everson R, Ruger S (2012) Weakly supervised joint sentiment-topic detection from text. IEEE Trans Knowl Data Eng 24(6):1134–1145CrossRef Lin C, He Y, Everson R, Ruger S (2012) Weakly supervised joint sentiment-topic detection from text. IEEE Trans Knowl Data Eng 24(6):1134–1145CrossRef
Zurück zum Zitat Lin C, Lee Y, Yu C, Chen H (2014) Exploring ensemble of models in taxonomy-based cross-domain sentiment classification. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management—CIKM’14, pp 1279–1288 Lin C, Lee Y, Yu C, Chen H (2014) Exploring ensemble of models in taxonomy-based cross-domain sentiment classification. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management—CIKM’14, pp 1279–1288
Zurück zum Zitat Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool publishers Liu B (2012) Sentiment analysis and opinion mining. Morgan and Claypool publishers
Zurück zum Zitat Liu L, Nie X, Wang H (2012) Toward a fuzzy domain sentiment ontology tree for sentiment analysis. In: Proceedings of the 5th international congress on image and signal processing (CISP 2012), pp 1620–1624 Liu L, Nie X, Wang H (2012) Toward a fuzzy domain sentiment ontology tree for sentiment analysis. In: Proceedings of the 5th international congress on image and signal processing (CISP 2012), pp 1620–1624
Zurück zum Zitat Lu Y, Kong X, Quan X, Liu W, Xu Y (2010) Exploring the sentiment strength of user reviews. In: Proceedings of the international conference on Web-age information management (WAIM 2010), pp 471–482 Lu Y, Kong X, Quan X, Liu W, Xu Y (2010) Exploring the sentiment strength of user reviews. In: Proceedings of the international conference on Web-age information management (WAIM 2010), pp 471–482
Zurück zum Zitat Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the 32nd AAAI conference on artificial intelligence AAAI 2018, pp 5876–5883 Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the 32nd AAAI conference on artificial intelligence AAAI 2018, pp 5876–5883
Zurück zum Zitat Maas AL et al (2014) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics, pp 142–150 Maas AL et al (2014) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics, pp 142–150
Zurück zum Zitat Majumder N, Poria S, Peng H, Chhaya N, Cambria E, Gelbukh A (2019) Sentiment and sarcasm classification with multitask learning. IEEE Intell Syst 34(3):38–43CrossRef Majumder N, Poria S, Peng H, Chhaya N, Cambria E, Gelbukh A (2019) Sentiment and sarcasm classification with multitask learning. IEEE Intell Syst 34(3):38–43CrossRef
Zurück zum Zitat Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford corenlp natural language processing toolkit. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics: system demonstrations, pp 55–60 Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford corenlp natural language processing toolkit. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics: system demonstrations, pp 55–60
Zurück zum Zitat Mcdonald R, Hannan K, Neylon T, Wells M, Reynar J (2007) Structured models for fine-to-coarse sentiment analysis. In: Proceedings of the 45th annual meeting of the association of computational linguistics, 432-439 Mcdonald R, Hannan K, Neylon T, Wells M, Reynar J (2007) Structured models for fine-to-coarse sentiment analysis. In: Proceedings of the 45th annual meeting of the association of computational linguistics, 432-439
Zurück zum Zitat Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J Electr Eng 5(4):1093–1113CrossRef Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J Electr Eng 5(4):1093–1113CrossRef
Zurück zum Zitat Mihalcea R, Banea C, Wiebe J (2007) Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th annual meeting of the Association for Computational Linguistics, pp 976–983 Mihalcea R, Banea C, Wiebe J (2007) Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th annual meeting of the Association for Computational Linguistics, pp 976–983
Zurück zum Zitat Moghaddam S, Ester M (2013) The FLDA Model for aspect-based opinion mining: addressing the cold start problem categories and subject descriptors. In: Proceedings of the international World Wide Web conferences steering committee, pp 909–918 Moghaddam S, Ester M (2013) The FLDA Model for aspect-based opinion mining: addressing the cold start problem categories and subject descriptors. In: Proceedings of the international World Wide Web conferences steering committee, pp 909–918
Zurück zum Zitat Moghaddam S, Jamali M, Ester M (2012) ETF: extended tensor factorization model for personalizing prediction of review helpfulness categories and subject descriptors. In: Proceedings of the 5th ACM international conference on web search and data mining, pp 163–172 Moghaddam S, Jamali M, Ester M (2012) ETF: extended tensor factorization model for personalizing prediction of review helpfulness categories and subject descriptors. In: Proceedings of the 5th ACM international conference on web search and data mining, pp 163–172
Zurück zum Zitat Mukherjee S, Joshi S (2013) Sentiment aggregation using conceptnet ontology. In: Proceedings of the sixth international joint conference on natural language processing, pp 570–578 Mukherjee S, Joshi S (2013) Sentiment aggregation using conceptnet ontology. In: Proceedings of the sixth international joint conference on natural language processing, pp 570–578
Zurück zum Zitat Mukherjee S, Joshi S (2014) Author-specific sentiment aggregation for polarity prediction of reviews. In: Proceedings of the 9th edition of the language resources and evaluation conference (LREC 2014), pp 3092–3099 Mukherjee S, Joshi S (2014) Author-specific sentiment aggregation for polarity prediction of reviews. In: Proceedings of the 9th edition of the language resources and evaluation conference (LREC 2014), pp 3092–3099
Zurück zum Zitat Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on World Wide Web (IW3C2), pp 191–200 Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on World Wide Web (IW3C2), pp 191–200
Zurück zum Zitat Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M (2013) Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 632–640 Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M (2013) Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 632–640
Zurück zum Zitat Mullen T, Collier N (2004) Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the 9th conference on empirical methods in natural language processing (EMNLP-04), pp 412–418 Mullen T, Collier N (2004) Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the 9th conference on empirical methods in natural language processing (EMNLP-04), pp 412–418
Zurück zum Zitat Nakayama Y, Fujii A (2015) Extracting condition-opinion relations toward fine-grained opinion mining. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 622–631 Nakayama Y, Fujii A (2015) Extracting condition-opinion relations toward fine-grained opinion mining. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 622–631
Zurück zum Zitat Narayanan R, Liu B, Choudhary A (2009) Sentiment analysis of conditional sentences. In: Proceedings of the conference on empirical methods in natural language processing, pp 180–189 Narayanan R, Liu B, Choudhary A (2009) Sentiment analysis of conditional sentences. In: Proceedings of the conference on empirical methods in natural language processing, pp 180–189
Zurück zum Zitat Nassirtoussi AK, Aghabozorgi S, Wah TY, Ngo DCL (2015) Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst Appl 42:306–324CrossRef Nassirtoussi AK, Aghabozorgi S, Wah TY, Ngo DCL (2015) Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst Appl 42:306–324CrossRef
Zurück zum Zitat Neviarouskaya A, Prendinger H, Ishizuka M (2011) SentiFul: a lexicon for sentiment analysis. IEEE Trans Affect Comput 2(1):22–36CrossRef Neviarouskaya A, Prendinger H, Ishizuka M (2011) SentiFul: a lexicon for sentiment analysis. IEEE Trans Affect Comput 2(1):22–36CrossRef
Zurück zum Zitat Nielsen FA (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903 Nielsen FA (2011) A new ANEW: evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:​1103.​2903
Zurück zum Zitat Nishikawa H, Hasegawa T, Matsuo Y, Kikui G (2010) Opinion summarization with integer linear programming formulation for sentence extraction and ordering. In: Proceedings of the 23rd international conference on computational linguistics, pp 910–918 Nishikawa H, Hasegawa T, Matsuo Y, Kikui G (2010) Opinion summarization with integer linear programming formulation for sentence extraction and ordering. In: Proceedings of the 23rd international conference on computational linguistics, pp 910–918
Zurück zum Zitat O’Connor B, Krieger M, Ahn D (2010) TweetMotif: exploratory search and topic summarization for Twitter. In: Proceedings of the fourth international AAAI conference on weblogs and social media, pp 384–385 O’Connor B, Krieger M, Ahn D (2010) TweetMotif: exploratory search and topic summarization for Twitter. In: Proceedings of the fourth international AAAI conference on weblogs and social media, pp 384–385
Zurück zum Zitat Ohana B, Delany SJ, Tierney B (2012) A Case-based approach to cross-domain sentiment classification. In: proceedings of the international conference on case-based reasoning, pp 284–296 Ohana B, Delany SJ, Tierney B (2012) A Case-based approach to cross-domain sentiment classification. In: proceedings of the international conference on case-based reasoning, pp 284–296
Zurück zum Zitat Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics, pp 309–319 Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics, pp 309–319
Zurück zum Zitat Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In: Proceedings of the NAACL-HLT. Association for Computational Linguistics, pp 497–501 Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In: Proceedings of the NAACL-HLT. Association for Computational Linguistics, pp 497–501
Zurück zum Zitat Pan SJ, Ni X, Sun J, Yang Q, Chen Z (2010) Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th international conference on World Wide Web—WWW’10, pp 751–760 Pan SJ, Ni X, Sun J, Yang Q, Chen Z (2010) Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th international conference on World Wide Web—WWW’10, pp 751–760
Zurück zum Zitat Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, pp 271–278 Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, pp 271–278
Zurück zum Zitat Pang B, Lee L (2005) Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint arXiv:cs/0506075 Pang B, Lee L (2005) Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint arXiv:​cs/​0506075
Zurück zum Zitat Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRef Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRef
Zurück zum Zitat Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol 10, pp 79–86 Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol 10, pp 79–86
Zurück zum Zitat Pennebaker JW, Boyd RL, Jordan K, Blackburn K (2015) The development and psychometric properties of in LIWC2015. University of Texas at Austin, Austin Pennebaker JW, Boyd RL, Jordan K, Blackburn K (2015) The development and psychometric properties of in LIWC2015. University of Texas at Austin, Austin
Zurück zum Zitat Ponomareva N, Thelwall M (2012) Biographies or blenders: which resource is best for cross-domain sentiment analysis? In: Proceedings of the international conference on intelligent text processing and computational linguistics, pp 488–499 Ponomareva N, Thelwall M (2012) Biographies or blenders: which resource is best for cross-domain sentiment analysis? In: Proceedings of the international conference on intelligent text processing and computational linguistics, pp 488–499
Zurück zum Zitat Ponomareva N, Thelwall M (2012) Do neighbours help? An exploration of graph-based algorithms for cross-domain sentiment classification. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pp 655–665 Ponomareva N, Thelwall M (2012) Do neighbours help? An exploration of graph-based algorithms for cross-domain sentiment classification. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pp 655–665
Zurück zum Zitat Ponomareva N, Thelwall M (2013) Semi-supervised vs. cross-domain graphs for sentiment analysis. In: Proceedings of recent advances in natural language processing, pp 571–578 Ponomareva N, Thelwall M (2013) Semi-supervised vs. cross-domain graphs for sentiment analysis. In: Proceedings of recent advances in natural language processing, pp 571–578
Zurück zum Zitat Popescu A, Etzioni O (2005) Extracting product features and opinions from reviews. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing (HLT/EMNLP), pp 339–346 Popescu A, Etzioni O (2005) Extracting product features and opinions from reviews. In: Proceedings of human language technology conference and conference on empirical methods in natural language processing (HLT/EMNLP), pp 339–346
Zurück zum Zitat Poria S, Gelbukh A, Hussain A, Howard N, Das D, Bandyopadhay S (2013) Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intell Syst 28(2):31–38CrossRef Poria S, Gelbukh A, Hussain A, Howard N, Das D, Bandyopadhay S (2013) Enhanced SenticNet with affective labels for concept-based opinion mining. IEEE Intell Syst 28(2):31–38CrossRef
Zurück zum Zitat Poria S, Cambria E, Hazarika D, Vij P (2016) A deeper look into sarcastic tweets using deep convolutional neural networks. In: Proceedings of the 26th international conference on computational linguistics (COLING 2016), pp 1601–1612 Poria S, Cambria E, Hazarika D, Vij P (2016) A deeper look into sarcastic tweets using deep convolutional neural networks. In: Proceedings of the 26th international conference on computational linguistics (COLING 2016), pp 1601–1612
Zurück zum Zitat Ptáček T, Habernal I, Hong J (2014) Sarcasm detection on czech and english twitter. In: Proceedings of the COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 213–223 Ptáček T, Habernal I, Hong J (2014) Sarcasm detection on czech and english twitter. In: Proceedings of the COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 213–223
Zurück zum Zitat Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st international joint conference on artificial intelligence, pp 1199–1204 Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st international joint conference on artificial intelligence, pp 1199–1204
Zurück zum Zitat Qiu X, Zhang Q, Huang X (2013) FudanNLP: a Toolkit for Chinese natural language processing. In: Proceedings of the 51st annual meeting of the Association for Computational Linguistics, pp 49–54 Qiu X, Zhang Q, Huang X (2013) FudanNLP: a Toolkit for Chinese natural language processing. In: Proceedings of the 51st annual meeting of the Association for Computational Linguistics, pp 49–54
Zurück zum Zitat Rabelo JCB, Prudêncio RBC, Barros FA (2012) Using link structure to infer opinions in social networks. In: Proceedings of the IEEE international conference on systems, man, and cybernetics (SMC), pp 681–685 Rabelo JCB, Prudêncio RBC, Barros FA (2012) Using link structure to infer opinions in social networks. In: Proceedings of the IEEE international conference on systems, man, and cybernetics (SMC), pp 681–685
Zurück zum Zitat Radev DR et al (2003) Evaluation challenges in large-scale document summarization. In: Proceedings of the 41st annual meeting on Association for Computational Linguistics, pp 375–382 Radev DR et al (2003) Evaluation challenges in large-scale document summarization. In: Proceedings of the 41st annual meeting on Association for Computational Linguistics, pp 375–382
Zurück zum Zitat Rastogi A, Mehrotra M (2018) Impact of behavioral and textual features on opinion spam detection. In: Proceedings of the second international conference on intelligent computing and control systems (ICICCS 2018) IEEE, pp 852–857 Rastogi A, Mehrotra M (2018) Impact of behavioral and textual features on opinion spam detection. In: Proceedings of the second international conference on intelligent computing and control systems (ICICCS 2018) IEEE, pp 852–857
Zurück zum Zitat Rehurek R, Sojka P (2010) Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks, pp 45–50 Rehurek R, Sojka P (2010) Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks, pp 45–50
Zurück zum Zitat Remus R (2012) Domain adaptation using domain similarity- and domain complexity-based instance selection for cross-domain sentiment analysis. In: Proceedings of the 12th international conference on data mining workshops domain, IEEE computer society, pp 717–723. https://doi.org/10.1109/ICDMW.2012.46 Remus R (2012) Domain adaptation using domain similarity- and domain complexity-based instance selection for cross-domain sentiment analysis. In: Proceedings of the 12th international conference on data mining workshops domain, IEEE computer society, pp 717–723. https://​doi.​org/​10.​1109/​ICDMW.​2012.​46
Zurück zum Zitat Roy SD, Mei T, Zeng W, Li S (2012) SocialTransfer: cross-domain transfer learning from social streams for media applications. In: Proceedings of the 20th ACM international conference on multimedia, pp 649–658 Roy SD, Mei T, Zeng W, Li S (2012) SocialTransfer: cross-domain transfer learning from social streams for media applications. In: Proceedings of the 20th ACM international conference on multimedia, pp 649–658
Zurück zum Zitat Sanju P, Mirnalinee TT (2014) Construction of enhanced sentiment sensitive thesaurus for cross domain sentiment classification using Wiktionary. In: Proceedings of the third international conference on soft computing for problem solving, pp 195–206. https://doi.org/10.1007/978-81-322-1768-8 Sanju P, Mirnalinee TT (2014) Construction of enhanced sentiment sensitive thesaurus for cross domain sentiment classification using Wiktionary. In: Proceedings of the third international conference on soft computing for problem solving, pp 195–206. https://​doi.​org/​10.​1007/​978-81-322-1768-8
Zurück zum Zitat Satapathy R, Guerreiro C, Chaturvedi I, Cambria E (2017) Phonetic-based microtext normalization for Twitter sentiment analysis. In: Proceedings of the IEEE international conference on data mining workshops (ICDMW), pp 407–413. https://doi.org/10.1109/ICDMW.2017.59 Satapathy R, Guerreiro C, Chaturvedi I, Cambria E (2017) Phonetic-based microtext normalization for Twitter sentiment analysis. In: Proceedings of the IEEE international conference on data mining workshops (ICDMW), pp 407–413. https://​doi.​org/​10.​1109/​ICDMW.​2017.​59
Zurück zum Zitat Satapathy R, Cambria E, Nanetti A, Hussain A (2020) A review of shorthand systems: from brachygraphy to microtext and beyond. Cogn Comput Satapathy R, Cambria E, Nanetti A, Hussain A (2020) A review of shorthand systems: from brachygraphy to microtext and beyond. Cogn Comput
Zurück zum Zitat Singh SK, Sachan MK (2019) SentiVerb system: classification of social media text using sentiment analysis. Multimed Tools Appl 78(22):32109–32136CrossRef Singh SK, Sachan MK (2019) SentiVerb system: classification of social media text using sentiment analysis. Multimed Tools Appl 78(22):32109–32136CrossRef
Zurück zum Zitat Socher R, et al (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642 Socher R, et al (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642
Zurück zum Zitat Stanik C, Haering M, Maalej W (2019) Classifying multilingual user feedback using traditional machine learning and deep learning. In: Proceedings of the IEEE 27th international requirements engineering conference workshops (REW 2019). IEEE, pp 220–226. https://doi.org/10.1109/REW.2019.00046 Stanik C, Haering M, Maalej W (2019) Classifying multilingual user feedback using traditional machine learning and deep learning. In: Proceedings of the IEEE 27th international requirements engineering conference workshops (REW 2019). IEEE, pp 220–226. https://​doi.​org/​10.​1109/​REW.​2019.​00046
Zurück zum Zitat Stone PJ, Dunphy DC, Smith MS (1966) The general inquirer: a computer approach to content analysis Stone PJ, Dunphy DC, Smith MS (1966) The general inquirer: a computer approach to content analysis
Zurück zum Zitat Taboada M, Grieve J (2004) Analyzing appraisal automatically classifying sentiment. In: Proceedings of the AAAI spring symposium on exploring attitude and affect in text Stanford, pp 158–161 Taboada M, Grieve J (2004) Analyzing appraisal automatically classifying sentiment. In: Proceedings of the AAAI spring symposium on exploring attitude and affect in text Stanford, pp 158–161
Zurück zum Zitat Taboada M, Brooke J, Tofilosk M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307CrossRef Taboada M, Brooke J, Tofilosk M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307CrossRef
Zurück zum Zitat Tackstrom O, Mcdonald R (2008) Semi-supervised latent variable models for sentence-level sentiment analysis. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies, pp 569–574 Tackstrom O, Mcdonald R (2008) Semi-supervised latent variable models for sentence-level sentiment analysis. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies, pp 569–574
Zurück zum Zitat Tan S, Cheng X, Wang Y, Xu H (2009) Adapting Naive Bayes to domain adaptation for sentiment analysis. In: Proceedings of the European conference on information retrieval in advances in information retrieval, pp 337–349 Tan S, Cheng X, Wang Y, Xu H (2009) Adapting Naive Bayes to domain adaptation for sentiment analysis. In: Proceedings of the European conference on information retrieval in advances in information retrieval, pp 337–349
Zurück zum Zitat Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P (2011) User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD-11), pp 1397–1405 Tan C, Lee L, Tang J, Jiang L, Zhou M, Li P (2011) User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD-11), pp 1397–1405
Zurück zum Zitat Tang D, Wei F, Qin B, Zhou M, Liu T (2014) Building large-scale Twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 172–182 Tang D, Wei F, Qin B, Zhou M, Liu T (2014) Building large-scale Twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 172–182
Zurück zum Zitat Tang D, Qin B, Wei F, Dong L, Liu T, Zhou M (2015) A joint segmentation and classification framework for sentence level sentiment classification. IEEE/ACM Trans Audio Speech Lang Process 23(11):1750–1761CrossRef Tang D, Qin B, Wei F, Dong L, Liu T, Zhou M (2015) A joint segmentation and classification framework for sentence level sentiment classification. IEEE/ACM Trans Audio Speech Lang Process 23(11):1750–1761CrossRef
Zurück zum Zitat Thelwall M, Buckley K, Paltoglou G, Cai D (2010) Sentiment strength detection in short informal text. J Am Soc Inform Sci Technol 61(12):2544–2558CrossRef Thelwall M, Buckley K, Paltoglou G, Cai D (2010) Sentiment strength detection in short informal text. J Am Soc Inform Sci Technol 61(12):2544–2558CrossRef
Zurück zum Zitat Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social web 1. J Am Soc Inform Sci Technol 63(1):163–173CrossRef Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social web 1. J Am Soc Inform Sci Technol 63(1):163–173CrossRef
Zurück zum Zitat Toutanova K, Klein D, Manning CD, Singer Y (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the conference of the North American chapter of the Association for Computational Linguistics on human language technology, vol 1. Association for Computational Linguistics, pp 173–180 Toutanova K, Klein D, Manning CD, Singer Y (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the conference of the North American chapter of the Association for Computational Linguistics on human language technology, vol 1. Association for Computational Linguistics, pp 173–180
Zurück zum Zitat Tsai AC, Wu C, Tsai RT, Hsu JY (2013) Building a concept-level sentiment on commonsense knowledge. IEEE Intell Syst 28(2):22–30CrossRef Tsai AC, Wu C, Tsai RT, Hsu JY (2013) Building a concept-level sentiment on commonsense knowledge. IEEE Intell Syst 28(2):22–30CrossRef
Zurück zum Zitat Tsai Y-L, Tsai RT-H, Chueh C-H, Chang S-C (2014) Cross-domain opinion word identification with query-by-committee active learning. In: Proceedings of the international conference on technologies and applications of artificial intelligence. Springer, Cham, pp 334–343. https://doi.org/10.1007/978-3-319-13987-6_31 Tsai Y-L, Tsai RT-H, Chueh C-H, Chang S-C (2014) Cross-domain opinion word identification with query-by-committee active learning. In: Proceedings of the international conference on technologies and applications of artificial intelligence. Springer, Cham, pp 334–343. https://​doi.​org/​10.​1007/​978-3-319-13987-6_​31
Zurück zum Zitat Tsakalidis A, Papadopoulos S, Kompatsiaris I (2014) An ensemble model for cross-domain polarity classification on Twitter. In: Proceedings of the international conference on web information systems engineering, pp 168–177 Tsakalidis A, Papadopoulos S, Kompatsiaris I (2014) An ensemble model for cross-domain polarity classification on Twitter. In: Proceedings of the international conference on web information systems engineering, pp 168–177
Zurück zum Zitat Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics (ACL), pp 417–424 Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics (ACL), pp 417–424
Zurück zum Zitat Velikovich L, Blair-goldensohn S, Hannan K, McDonald R (2010) The viability of web-derived polarity lexicons. In: Proceedings of the human language technologies: the 2010 annual conference of the North American chapter of the Association for Computational Linguistics, pp 777–785 Velikovich L, Blair-goldensohn S, Hannan K, McDonald R (2010) The viability of web-derived polarity lexicons. In: Proceedings of the human language technologies: the 2010 annual conference of the North American chapter of the Association for Computational Linguistics, pp 777–785
Zurück zum Zitat Vilares D, Peng H, Satapathy R, Cambria E (2018) BabelSenticNet: a commonsense reasoning framework for multilingual sentiment analysis. In: Proceedings of the 2018 IEEE symposium series on computational intelligence (SSCI 2018), pp 1292–1298. https://doi.org/10.1109/SSCI.2018.8628718 Vilares D, Peng H, Satapathy R, Cambria E (2018) BabelSenticNet: a commonsense reasoning framework for multilingual sentiment analysis. In: Proceedings of the 2018 IEEE symposium series on computational intelligence (SSCI 2018), pp 1292–1298. https://​doi.​org/​10.​1109/​SSCI.​2018.​8628718
Zurück zum Zitat Walker MA, Anand P, Tree JEF, Abbott R, King J (2012) A corpus for research on deliberation and debate. In: Proceedings of the 8th international conference on language resources and evaluation (LREC-2012), pp 812–817 Walker MA, Anand P, Tree JEF, Abbott R, King J (2012) A corpus for research on deliberation and debate. In: Proceedings of the 8th international conference on language resources and evaluation (LREC-2012), pp 812–817
Zurück zum Zitat Wan X (2008) Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 553–561 Wan X (2008) Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 553–561
Zurück zum Zitat Wang J, Lee C (2011) Unsupervised opinion phrase extraction and rating in Chinese blog posts. In: Proceedings of the IEEE international conference on privacy, security, risk, and trust, and IEEE international conference on social computing, pp 820–823 Wang J, Lee C (2011) Unsupervised opinion phrase extraction and rating in Chinese blog posts. In: Proceedings of the IEEE international conference on privacy, security, risk, and trust, and IEEE international conference on social computing, pp 820–823
Zurück zum Zitat Wang S, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp 90–94 Wang S, Manning CD (2012) Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp 90–94
Zurück zum Zitat Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD conference on knowledge discovery and data mining (KDD’2010), pp 783–792 Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD conference on knowledge discovery and data mining (KDD’2010), pp 783–792
Zurück zum Zitat Wang L, Liu K, Cao Z, Zhao J, Melo GD (2015) Sentiment-aspect extraction based on restricted Boltzmann machines. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing, pp 616–625 Wang L, Liu K, Cao Z, Zhao J, Melo GD (2015) Sentiment-aspect extraction based on restricted Boltzmann machines. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing, pp 616–625
Zurück zum Zitat Wang L, Niu J, Song H, Atiquzzaman M (2018b) SentiRelated: a cross-domain sentiment classification algorithm for short texts through sentiment related index. J Netw Comput Appl 101:111–119CrossRef Wang L, Niu J, Song H, Atiquzzaman M (2018b) SentiRelated: a cross-domain sentiment classification algorithm for short texts through sentiment related index. J Netw Comput Appl 101:111–119CrossRef
Zurück zum Zitat Wei B, Pal C (2010) Cross lingual adaptation: an experiment on sentiment classifications. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics, pp 258–262 Wei B, Pal C (2010) Cross lingual adaptation: an experiment on sentiment classifications. In: Proceedings of the 48th annual meeting of the Association for Computational Linguistics, pp 258–262
Zurück zum Zitat Whissell CM (1989) The dictionary of affect in language. In: The measurement of emotions, Academic Press, pp 113–131 Whissell CM (1989) The dictionary of affect in language. In: The measurement of emotions, Academic Press, pp 113–131
Zurück zum Zitat Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM international conference on information and knowledge management. ACM, pp 625–631 Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM international conference on information and knowledge management. ACM, pp 625–631
Zurück zum Zitat Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the human language technology conference and conference on empirical methods in natural language processing (HLT/EMNLP), pp 347–354 Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the human language technology conference and conference on empirical methods in natural language processing (HLT/EMNLP), pp 347–354
Zurück zum Zitat Xia R, Zong C, Hu X, Cambria E (2013) Feature ensemble plus sample selection: domain adaptation classification. IEEE Intell Syst 28(3):10–18CrossRef Xia R, Zong C, Hu X, Cambria E (2013) Feature ensemble plus sample selection: domain adaptation classification. IEEE Intell Syst 28(3):10–18CrossRef
Zurück zum Zitat Xueke X, Xueqi C, Songbo T, Yue L, Huawei S (2013) Aspect-level opinion mining of online customer reviews. China Commun 10(3):25–41CrossRef Xueke X, Xueqi C, Songbo T, Yue L, Huawei S (2013) Aspect-level opinion mining of online customer reviews. China Commun 10(3):25–41CrossRef
Zurück zum Zitat Yang B, Cardie C (2014) Context-aware learning for sentence-level sentiment analysis with posterior regularization. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics, pp 325–335 Yang B, Cardie C (2014) Context-aware learning for sentence-level sentiment analysis with posterior regularization. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics, pp 325–335
Zurück zum Zitat Yessenalina A, Yue Y, Cardie C (2010) Multi-level structured models for document-level sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1046–1056 Yessenalina A, Yue Y, Cardie C (2010) Multi-level structured models for document-level sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1046–1056
Zurück zum Zitat Yu H, Hatzivassiloglou V (2003) Towards answering opinion questions : separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the conference on empirical methods in natural language processing, pp 129–136 Yu H, Hatzivassiloglou V (2003) Towards answering opinion questions : separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the conference on empirical methods in natural language processing, pp 129–136
Zurück zum Zitat Yu J, Jiang J (2016) Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing, pp 236–246 Yu J, Jiang J (2016) Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing, pp 236–246
Zurück zum Zitat Zhai Z, Liu B, Xu H, Jia P (2011) Clustering product features for opinion mining. In: Proceedings of the 4th ACM international conference on web search and data mining, pp 347–354 Zhai Z, Liu B, Xu H, Jia P (2011) Clustering product features for opinion mining. In: Proceedings of the 4th ACM international conference on web search and data mining, pp 347–354
Zurück zum Zitat Zhai Z, Liu B, Wang J, Xu H, Jia P (2012) Product feature grouping for opinion mining. IEEE Intell Syst 27(4):37–44CrossRef Zhai Z, Liu B, Wang J, Xu H, Jia P (2012) Product feature grouping for opinion mining. IEEE Intell Syst 27(4):37–44CrossRef
Zurück zum Zitat Zhang Z (2008) Weighing stars: aggregating online product. IEEE Intell Syst 23(5):42–49CrossRef Zhang Z (2008) Weighing stars: aggregating online product. IEEE Intell Syst 23(5):42–49CrossRef
Zurück zum Zitat Zhang RUI, Wang Z, Yin KAI, Huang Z (2019) Emotional text generation based on cross-domain sentiment transfer. IEEE Access 7:100081–100089CrossRef Zhang RUI, Wang Z, Yin KAI, Huang Z (2019) Emotional text generation based on cross-domain sentiment transfer. IEEE Access 7:100081–100089CrossRef
Zurück zum Zitat Zhao W, Peng H, Eger S, Cambria E, Yang M (2019) Towards scalable and reliable capsule networks for challenging NLP applications. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1549–1559. https://doi.org/10.18653/v1/P19-1150 Zhao W, Peng H, Eger S, Cambria E, Yang M (2019) Towards scalable and reliable capsule networks for challenging NLP applications. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1549–1559. https://​doi.​org/​10.​18653/​v1/​P19-1150
Zurück zum Zitat Zheng X, Lin Z, Wang X, Lin K, Song M (2014) Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification. Knowl-Based Syst 61:29–47CrossRef Zheng X, Lin Z, Wang X, Lin K, Song M (2014) Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification. Knowl-Based Syst 61:29–47CrossRef
Zurück zum Zitat Zhou H, Song F (2012) Aspect-level sentiment analysis based on a generalized probabilistic topic and syntax model. In: Proceedings of the twenty-eighth international Florida artificial intelligence research society conference, pp 241–244 Zhou H, Song F (2012) Aspect-level sentiment analysis based on a generalized probabilistic topic and syntax model. In: Proceedings of the twenty-eighth international Florida artificial intelligence research society conference, pp 241–244
Zurück zum Zitat Zhu Z, Dai D, Ding Y, Qian J, Li S (2013) Employing emotion keywords to improve cross-domain sentiment classification. In: Proceedings of the workshop on Chinese lexical semantics, pp 64–71 Zhu Z, Dai D, Ding Y, Qian J, Li S (2013) Employing emotion keywords to improve cross-domain sentiment classification. In: Proceedings of the workshop on Chinese lexical semantics, pp 64–71
Zurück zum Zitat Zhu X, Ghahramani Z (2002) Learning from labeled and unlabeled data with label propagation Zhu X, Ghahramani Z (2002) Learning from labeled and unlabeled data with label propagation
Zurück zum Zitat Zhu J, Wang Q (2015) NiuParser: a Chinese syntactic and semantic parsing toolkit. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing: system demonstrations, pp 145–150 Zhu J, Wang Q (2015) NiuParser: a Chinese syntactic and semantic parsing toolkit. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing: system demonstrations, pp 145–150
Zurück zum Zitat Zhu J, Wang H, Zhu M, Tsou BK, Ma M (2011) Aspect-based opinion polling from customer reviews. IEEE Trans Affect Comput 2(1):37–49CrossRef Zhu J, Wang H, Zhu M, Tsou BK, Ma M (2011) Aspect-based opinion polling from customer reviews. IEEE Trans Affect Comput 2(1):37–49CrossRef
Metadaten
Titel
360 degree view of cross-domain opinion classification: a survey
verfasst von
Rahul Kumar Singh
Manoj Kumar Sachan
R. B. Patel
Publikationsdatum
06.08.2020
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 2/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-020-09884-9

Weitere Artikel der Ausgabe 2/2021

Artificial Intelligence Review 2/2021 Zur Ausgabe

Premium Partner