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Erschienen in: Cognitive Computation 4/2016

01.08.2016 | Erratum

Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

verfasst von: Kia Dashtipour, Soujanya Poria, Amir Hussain, Erik Cambria, Ahmad Y. A. Hawalah, Alexander Gelbukh, Qiang Zhou

Erschienen in: Cognitive Computation | Ausgabe 4/2016

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Unfortunately, the original version of the article has been published with few errors in Abstract, Conclusion, Acknowledgment, and References. …

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Literatur
1.
Zurück zum Zitat Agarwal B, Poria S, Mittal N, Gelbukh A, Hussain A. Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cogn Comput. 2015;7(4):487–99.CrossRef Agarwal B, Poria S, Mittal N, Gelbukh A, Hussain A. Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cogn Comput. 2015;7(4):487–99.CrossRef
2.
Zurück zum Zitat Ahmad K, Cheng D, Almas Y. Multi-lingual sentiment analysis of financial news streams. In: Proceedings of the 1st international conference on grid in finance; 2006. Ahmad K, Cheng D, Almas Y. Multi-lingual sentiment analysis of financial news streams. In: Proceedings of the 1st international conference on grid in finance; 2006.
3.
Zurück zum Zitat Al-Ayyoub M, Essa SB, Alsmadi I. Lexicon-based sentiment analysis of arabic tweets. Int J Soc Netw Min. 2015;2:101–14.CrossRef Al-Ayyoub M, Essa SB, Alsmadi I. Lexicon-based sentiment analysis of arabic tweets. Int J Soc Netw Min. 2015;2:101–14.CrossRef
4.
Zurück zum Zitat Balahur A, Turchi M. Multilingual sentiment analysis using machine translation? In: Proceedings of the 3rd workshop in computational approaches to subjectivity and sentiment analysis. Association for Computational Linguistics; 2012, p. 52–60. Balahur A, Turchi M. Multilingual sentiment analysis using machine translation? In: Proceedings of the 3rd workshop in computational approaches to subjectivity and sentiment analysis. Association for Computational Linguistics; 2012, p. 52–60.
5.
Zurück zum Zitat Balahur A, Turchi M. Improving sentiment analysis in twitter using multilingual machine translated data. In: RANLP; 2013, p. 49–55. Balahur A, Turchi M. Improving sentiment analysis in twitter using multilingual machine translated data. In: RANLP; 2013, p. 49–55.
6.
Zurück zum Zitat Bautin M, Vijayarenu L, Skiena S. International sentiment analysis for news and blogs. In: ICWSM; 2008. Bautin M, Vijayarenu L, Skiena S. International sentiment analysis for news and blogs. In: ICWSM; 2008.
7.
Zurück zum Zitat Berger AL, Pietra VJD, Pietra SAD. A maximum entropy approach to natural language processing. Comput Linguist. 1996;22:39–71. Berger AL, Pietra VJD, Pietra SAD. A maximum entropy approach to natural language processing. Comput Linguist. 1996;22:39–71.
8.
Zurück zum Zitat Bhaskar J, Sruthi K, Nedungadi P. Enhanced sentiment analysis of informal textual communication in social media by considering objective words and intensifiers. In: Recent advances and innovations in engineering (ICRAIE), 2014. IEEE; 2014, p. 1–6. Bhaskar J, Sruthi K, Nedungadi P. Enhanced sentiment analysis of informal textual communication in social media by considering objective words and intensifiers. In: Recent advances and innovations in engineering (ICRAIE), 2014. IEEE; 2014, p. 1–6.
9.
Zurück zum Zitat Blitzer J, Dredze M, Pereira F, et al. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL; 2007, p. 440–47. Blitzer J, Dredze M, Pereira F, et al. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL; 2007, p. 440–47.
10.
Zurück zum Zitat Boiy E, Moens M-F. A machine learning approach to sentiment analysis in multilingual Web texts. Inf Retr. 2009;12:526–58.CrossRef Boiy E, Moens M-F. A machine learning approach to sentiment analysis in multilingual Web texts. Inf Retr. 2009;12:526–58.CrossRef
11.
Zurück zum Zitat Cambria E, Olsher D, Rajagopal D. SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In: AAAI, 2014, p. 1515–1521, Quebec City. Cambria E, Olsher D, Rajagopal D. SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In: AAAI, 2014, p. 1515–1521, Quebec City.
12.
Zurück zum Zitat Carroll TZJ. Unsupervised classification of sentiment and objectivity in Chinese text. In: Third international joint conference on natural language processing. 2008, p. 304. Carroll TZJ. Unsupervised classification of sentiment and objectivity in Chinese text. In: Third international joint conference on natural language processing. 2008, p. 304.
13.
Zurück zum Zitat Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol TIST. 2011;2:27. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol TIST. 2011;2:27.
14.
Zurück zum Zitat Chikersal P, Poria S, Cambria E. SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In: Proceedings of the international workshop on semantic evaluation (SemEval 2015). 2015. Chikersal P, Poria S, Cambria E. SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In: Proceedings of the international workshop on semantic evaluation (SemEval 2015). 2015.
15.
Zurück zum Zitat Croft WB, Lafferty J. Language modeling for information retrieval. Berlin: Springer; 2003.CrossRef Croft WB, Lafferty J. Language modeling for information retrieval. Berlin: Springer; 2003.CrossRef
16.
Zurück zum Zitat Cruz-Garcia IO, Gelbukh A, Sidorov G. Implicit aspect indicator extraction for aspect based opinion mining. Int J Comput Linguist Appl. 2014;5(2):135–52. Cruz-Garcia IO, Gelbukh A, Sidorov G. Implicit aspect indicator extraction for aspect based opinion mining. Int J Comput Linguist Appl. 2014;5(2):135–52.
17.
Zurück zum Zitat Das N, Ghosh S, Gonçalves T, Quaresma P. Comparison of different graph distance metrics for semantic text based classification. Polibits. 2014;49:51–7.CrossRef Das N, Ghosh S, Gonçalves T, Quaresma P. Comparison of different graph distance metrics for semantic text based classification. Polibits. 2014;49:51–7.CrossRef
18.
Zurück zum Zitat Denecke K. Using SentiWordNet for multilingual sentiment analysis. In: IEEE 24th international data engineering workshop, 2008. ICDEW 2008. IEEE; 2008, p. 507–12. Denecke K. Using SentiWordNet for multilingual sentiment analysis. In: IEEE 24th international data engineering workshop, 2008. ICDEW 2008. IEEE; 2008, p. 507–12.
19.
Zurück zum Zitat Duwairi RM, Qarqaz I (2014) Arabic sentiment analysis using supervised classification. In: 2014 international conference on future internet of things and cloud (FiCloud). IEEE; 2014. Duwairi RM, Qarqaz I (2014) Arabic sentiment analysis using supervised classification. In: 2014 international conference on future internet of things and cloud (FiCloud). IEEE; 2014.
20.
Zurück zum Zitat Evans DK, Ku L-W, Seki Y, Chen H–H, Kando N. Opinion analysis across languages: an overview of and observations from the NTCIR6 opinion analysis pilot task. In: Applications of fuzzy sets theory. Berlin: Springer; 2007, p. 456–63. Evans DK, Ku L-W, Seki Y, Chen H–H, Kando N. Opinion analysis across languages: an overview of and observations from the NTCIR6 opinion analysis pilot task. In: Applications of fuzzy sets theory. Berlin: Springer; 2007, p. 456–63.
21.
Zurück zum Zitat Ghorbel H, Jacot D. Further experiments in sentiment analysis of French movie reviews. In: Advances in intelligent web mastering–3. Berlin, Heidelberg: Springer; 2011, p. 19–28. Ghorbel H, Jacot D. Further experiments in sentiment analysis of French movie reviews. In: Advances in intelligent web mastering–3. Berlin, Heidelberg: Springer; 2011, p. 19–28.
22.
Zurück zum Zitat Ghosh M, Kar A. Unsupervised linguistic approach for sentiment classification from online reviews using SentiWordNet 3.0. Int J Eng Res Technol. 2013. Ghosh M, Kar A. Unsupervised linguistic approach for sentiment classification from online reviews using SentiWordNet 3.0. Int J Eng Res Technol. 2013.
23.
Zurück zum Zitat Habernal I, Ptácek T, Steinberger J. Sentiment analysis in Czech social media using supervised machine learning. In: Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis. 2013, p. 65–74. Habernal I, Ptácek T, Steinberger J. Sentiment analysis in Czech social media using supervised machine learning. In: Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis. 2013, p. 65–74.
24.
Zurück zum Zitat He Y, Zhou D. Self-training from labeled features for sentiment analysis. Inf Process Manag. 2011;47:606–16.CrossRef He Y, Zhou D. Self-training from labeled features for sentiment analysis. Inf Process Manag. 2011;47:606–16.CrossRef
25.
Zurück zum Zitat Holmes G, Donkin A, Witten IH. Weka: a machine learning workbench. In: Proceedings of the 1994 second Australian and New Zealand conference on intelligent information systems. IEEE; 1994, p. 357–61. Holmes G, Donkin A, Witten IH. Weka: a machine learning workbench. In: Proceedings of the 1994 second Australian and New Zealand conference on intelligent information systems. IEEE; 1994, p. 357–61.
26.
Zurück zum Zitat Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM; 2004, p. 168–77. Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM; 2004, p. 168–77.
27.
Zurück zum Zitat Jimenez S, Gonzalez FA, Gelbukh A. Soft cardinality in semantic text processing: experience of the SemEval international competitions. Polibits. 2015;51:63–72.CrossRef Jimenez S, Gonzalez FA, Gelbukh A. Soft cardinality in semantic text processing: experience of the SemEval international competitions. Polibits. 2015;51:63–72.CrossRef
28.
Zurück zum Zitat Liu B. Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge: Cambridge University Press; 2015.CrossRef Liu B. Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge: Cambridge University Press; 2015.CrossRef
29.
Zurück zum Zitat Liu Z, Dong X, Guan Y, Yang J. Reserved self-training: a semisupervised sentiment classification method for Chinese microblogs. In: Proceedings of IJCNLP; 2013. Liu Z, Dong X, Guan Y, Yang J. Reserved self-training: a semisupervised sentiment classification method for Chinese microblogs. In: Proceedings of IJCNLP; 2013.
30.
Zurück zum Zitat Mahyoub FHH, Siddiqui MA, Dahab MY. Building an Arabic sentiment lexicon using semi-supervised learning. J King Saud Univ Comput Inf Sci. 2014;26(4):417–24. Mahyoub FHH, Siddiqui MA, Dahab MY. Building an Arabic sentiment lexicon using semi-supervised learning. J King Saud Univ Comput Inf Sci. 2014;26(4):417–24.
31.
Zurück zum Zitat Manning CD, Raghavan P, Schütze H. Introduction to information retrieval. Cambridge: Cambridge University Press; 2008.CrossRef Manning CD, Raghavan P, Schütze H. Introduction to information retrieval. Cambridge: Cambridge University Press; 2008.CrossRef
32.
Zurück zum Zitat Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 2014;5:1093–113.CrossRef Medhat W, Hassan A, Korashy H. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 2014;5:1093–113.CrossRef
33.
Zurück zum Zitat Mirchev U, Last M. Multi-document summarization by extended graph text representation and importance refinement. Innov Doc Summ Tech Revolut Knowl Underst Revolut Knowl Underst. 2014; 28. Mirchev U, Last M. Multi-document summarization by extended graph text representation and importance refinement. Innov Doc Summ Tech Revolut Knowl Underst Revolut Knowl Underst. 2014; 28.
34.
Zurück zum Zitat Mizumoto K, Yanagimoto H, Yoshioka M. Sentiment analysis of stock market news with semi-supervised learning. In: 2012 IEEE/ACIS 11th international conference on computer and information science (ICIS). IEEE, 2012; p. 325–28. Mizumoto K, Yanagimoto H, Yoshioka M. Sentiment analysis of stock market news with semi-supervised learning. In: 2012 IEEE/ACIS 11th international conference on computer and information science (ICIS). IEEE, 2012; p. 325–28.
35.
Zurück zum Zitat Morency L-P, Mihalcea R, Doshi P. Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the 13th international conference on multimodal interfaces. ACM; 2011, p. 169–76. Morency L-P, Mihalcea R, Doshi P. Towards multimodal sentiment analysis: harvesting opinions from the web. In: Proceedings of the 13th international conference on multimodal interfaces. ACM; 2011, p. 169–76.
36.
Zurück zum Zitat Narayanan V, Arora I, Bhatia A. Fast and accurate sentiment classification using an enhanced Naive Bayes model. In: Intelligent data engineering and automated learning–IDEAL 2013. Berlin: Springer; 2013, p. 194–201. Narayanan V, Arora I, Bhatia A. Fast and accurate sentiment classification using an enhanced Naive Bayes model. In: Intelligent data engineering and automated learning–IDEAL 2013. Berlin: Springer; 2013, p. 194–201.
37.
Zurück zum Zitat Pang B, Lee L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics. Association for Computational Linguistics; 2004, p. 271. Pang B, Lee L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics. Association for Computational Linguistics; 2004, p. 271.
38.
Zurück zum Zitat Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10. Association for Computational Linguistics, 2002; p. 79–86. Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10. Association for Computational Linguistics, 2002; p. 79–86.
39.
Zurück zum Zitat Posadas-Durán J-P, Markov I, Gómez-Adorno H, Sidorov G, Batyrshin I, Gelbukh A, Pichardo-Lagunas O. Syntactic N-grams as features for the author profiling task. Notebook for PAN at CLEF 2015. CEUR Workshop Proceedings 1391; 2015. Posadas-Durán J-P, Markov I, Gómez-Adorno H, Sidorov G, Batyrshin I, Gelbukh A, Pichardo-Lagunas O. Syntactic N-grams as features for the author profiling task. Notebook for PAN at CLEF 2015. CEUR Workshop Proceedings 1391; 2015.
40.
Zurück zum Zitat Raina P. Sentiment analysis in news articles using sentic computing. In: 2013 IEEE 13th International conference on data mining workshops (ICDMW). IEEE; 2013, p. 959–62. Raina P. Sentiment analysis in news articles using sentic computing. In: 2013 IEEE 13th International conference on data mining workshops (ICDMW). IEEE; 2013, p. 959–62.
41.
Zurück zum Zitat Rajagopal D, Cambria E, Olsher D, Kwok K. A graph-based approach to commonsense concept extraction and semantic similarity detection. In: Proceedings of the 22nd international conference on world wide web companion. International World Wide Web Conferences Steering Committee; 2013, p. 565–70. Rajagopal D, Cambria E, Olsher D, Kwok K. A graph-based approach to commonsense concept extraction and semantic similarity detection. In: Proceedings of the 22nd international conference on world wide web companion. International World Wide Web Conferences Steering Committee; 2013, p. 565–70.
42.
Zurück zum Zitat Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst. 2015. Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst. 2015.
43.
Zurück zum Zitat Read J. Recognising affect in text using pointwise-mutual information. Unpubl. M Sc Diss. Univ. Sussex UK; 2004. Read J. Recognising affect in text using pointwise-mutual information. Unpubl. M Sc Diss. Univ. Sussex UK; 2004.
44.
Zurück zum Zitat Remus R, Quasthoff U, Heyer G. SentiWS-a publicly available German-language resource for sentiment analysis. In: LREC. 2010. Remus R, Quasthoff U, Heyer G. SentiWS-a publicly available German-language resource for sentiment analysis. In: LREC. 2010.
45.
Zurück zum Zitat Saraee M, Bagheri A. Feature selection methods in Persian sentiment analysis. In: Natural language processing and information systems. Springer; 2013, p. 303–308. Saraee M, Bagheri A. Feature selection methods in Persian sentiment analysis. In: Natural language processing and information systems. Springer; 2013, p. 303–308.
46.
Zurück zum Zitat Seki Y, Evans DK, Ku L-W, Sun L, Chen H–H, Kando N, Lin C-Y. Overview of multilingual opinion analysis task at NTCIR-7. In: Proceedings of the 7th NTCIR workshop meeting on evaluation of information access technologies: information retrieval, question answering, and cross-lingual information access. 2008, p. 185–203. Seki Y, Evans DK, Ku L-W, Sun L, Chen H–H, Kando N, Lin C-Y. Overview of multilingual opinion analysis task at NTCIR-7. In: Proceedings of the 7th NTCIR workshop meeting on evaluation of information access technologies: information retrieval, question answering, and cross-lingual information access. 2008, p. 185–203.
47.
Zurück zum Zitat Shi H–X, Li X-J. A sentiment analysis model for hotel reviews based on supervised learning. In: 2011 international conference on machine learning and cybernetics (ICMLC). IEEE; 2011, p. 950–54. Shi H–X, Li X-J. A sentiment analysis model for hotel reviews based on supervised learning. In: 2011 international conference on machine learning and cybernetics (ICMLC). IEEE; 2011, p. 950–54.
48.
Zurück zum Zitat Sidorov G. Should syntactic n-grams contain names of syntactic relations? Int J Comput Linguist Appl. 2014;5(2):25–47. Sidorov G. Should syntactic n-grams contain names of syntactic relations? Int J Comput Linguist Appl. 2014;5(2):25–47.
49.
Zurück zum Zitat Sidorov G, Miranda-Jiménez S, Viveros-Jiménez F, Gelbukh A, Castro-Sánchez N, Velásquez F, Díaz-Rangel I, Suárez-Guerra S, Treviño A, Gordon J. Empirical study of opinion mining in Spanish tweets. MICAI 2012. Lect Notes Comput Sci. 2012;7629:1–14. Sidorov G, Miranda-Jiménez S, Viveros-Jiménez F, Gelbukh A, Castro-Sánchez N, Velásquez F, Díaz-Rangel I, Suárez-Guerra S, Treviño A, Gordon J. Empirical study of opinion mining in Spanish tweets. MICAI 2012. Lect Notes Comput Sci. 2012;7629:1–14.
50.
Zurück zum Zitat Sidorov G, Velasquez F, Stamatatos E, Gelbukh A, Chanona-Hernández L. Syntactic n-grams as machine learning features for natural language processing. Expert Syst Appl. 2014;41(3):853–60.CrossRef Sidorov G, Velasquez F, Stamatatos E, Gelbukh A, Chanona-Hernández L. Syntactic n-grams as machine learning features for natural language processing. Expert Syst Appl. 2014;41(3):853–60.CrossRef
51.
Zurück zum Zitat Sindhwani V, Melville P. Document-word co-regularization for semi-supervised sentiment analysis. In: Eighth IEEE international conference on data mining, 2008. ICDM’08. IEEE; 2008, p. 1025–30. Sindhwani V, Melville P. Document-word co-regularization for semi-supervised sentiment analysis. In: Eighth IEEE international conference on data mining, 2008. ICDM’08. IEEE; 2008, p. 1025–30.
52.
Zurück zum Zitat Singh VK, Piryani R, Uddin A, Waila P, et al. Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches. In: 2013 5th international conference on knowledge and smart technology (KST). IEEE; 2013, p. 122–27. Singh VK, Piryani R, Uddin A, Waila P, et al. Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches. In: 2013 5th international conference on knowledge and smart technology (KST). IEEE; 2013, p. 122–27.
53.
Zurück zum Zitat Stone PJ, Dunphy DC, Smith MS. The general inquirer: a computer approach to content analysis; 1966. Stone PJ, Dunphy DC, Smith MS. The general inquirer: a computer approach to content analysis; 1966.
54.
Zurück zum Zitat Tan S, Zhang J. An empirical study of sentiment analysis for Chinese documents. Expert Syst Appl. 2008;34:2622–9.CrossRef Tan S, Zhang J. An empirical study of sentiment analysis for Chinese documents. Expert Syst Appl. 2008;34:2622–9.CrossRef
55.
Zurück zum Zitat Tong S, Koller D. Support vector machine active learning with applications to text classification. J Mach Learn Res. 2002;2:45–66. Tong S, Koller D. Support vector machine active learning with applications to text classification. J Mach Learn Res. 2002;2:45–66.
56.
Zurück zum Zitat Tromp E. Multilingual sentiment analysis on social media. Master’s Thesis, Dep. Math. Comput. Sci. Eindh. Univ. Technol.; 2011. Tromp E. Multilingual sentiment analysis on social media. Master’s Thesis, Dep. Math. Comput. Sci. Eindh. Univ. Technol.; 2011.
57.
Zurück zum Zitat Wan X. 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; 2008, p. 553–61. Wan X. 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; 2008, p. 553–61.
58.
Zurück zum Zitat Wang S, Li D, Song X, Wei Y, Li H. A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Syst Appl. 2011;38:8696–702.CrossRef Wang S, Li D, Song X, Wei Y, Li H. A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Syst Appl. 2011;38:8696–702.CrossRef
59.
Zurück zum Zitat Wiebe J, Mihalcea R. Word sense and subjectivity. In: Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics; 2006, p. 1065–72. Wiebe J, Mihalcea R. Word sense and subjectivity. In: Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics; 2006, p. 1065–72.
60.
Zurück zum Zitat Wong K-F, Xia Y, Xu R, Wu M, Li W. Pattern-based opinion mining for stock market trend prediction. Int J Comput Process Orient Lang. 2008;21(4):347–61.CrossRef Wong K-F, Xia Y, Xu R, Wu M, Li W. Pattern-based opinion mining for stock market trend prediction. Int J Comput Process Orient Lang. 2008;21(4):347–61.CrossRef
61.
Zurück zum Zitat Xia Y, Cambria E, Hussain A, Zhao H. Word polarity disambiguation using Bayesian model and opinion-level features. Cogn Comput. 2015;7(3):369–80.CrossRef Xia Y, Cambria E, Hussain A, Zhao H. Word polarity disambiguation using Bayesian model and opinion-level features. Cogn Comput. 2015;7(3):369–80.CrossRef
62.
Zurück zum Zitat Xia Y, Wang L, Wong K-F. Sentiment vector space model for lyric-based song sentiment classification. Int J Comput Process Orient Lang. 2008;21(4):331–45.CrossRef Xia Y, Wang L, Wong K-F. Sentiment vector space model for lyric-based song sentiment classification. Int J Comput Process Orient Lang. 2008;21(4):331–45.CrossRef
63.
Zurück zum Zitat Xia Y, Zhao T, Yao J, Jin P. Measuring Chinese-English crosslingual word similarity with HowNet and parallel corpus. In: Computational linguistics and intelligent text processing, 12th international conference, CICLing 2011, vol. 2. 2011, p. 221–33. Xia Y, Zhao T, Yao J, Jin P. Measuring Chinese-English crosslingual word similarity with HowNet and parallel corpus. In: Computational linguistics and intelligent text processing, 12th international conference, CICLing 2011, vol. 2. 2011, p. 221–33.
64.
Zurück zum Zitat Xia Y, Li X, Cambria E, Hussain A. A localization toolkit for SenticNet. In: 2014 IEEE international conference on data mining workshop (ICDMW). 2014, p. 403–8. Xia Y, Li X, Cambria E, Hussain A. A localization toolkit for SenticNet. In: 2014 IEEE international conference on data mining workshop (ICDMW). 2014, p. 403–8.
65.
Zurück zum Zitat Xia R, Zong C. Exploring the use of word relation features for sentiment classification. In: Proceedings of the 23rd international conference on computational linguistics: posters. Association for Computational Linguistics; 2010, p. 1336–44. Xia R, Zong C. Exploring the use of word relation features for sentiment classification. In: Proceedings of the 23rd international conference on computational linguistics: posters. Association for Computational Linguistics; 2010, p. 1336–44.
66.
Zurück zum Zitat Xu Y, Jones GJ, Li J, Wang B, Sun C. A study on mutual information-based feature selection for text categorization. J Comput Inf Syst. 2007;3:1007–12. Xu Y, Jones GJ, Li J, Wang B, Sun C. A study on mutual information-based feature selection for text categorization. J Comput Inf Syst. 2007;3:1007–12.
67.
Zurück zum Zitat Xu R, Wong K-F, Lu Q, Xia Y, Li W. Learning knowledge from relevant webpage for opinion analysis. In: IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, WI-IAT ‘08. 2008, p. 307–13. Xu R, Wong K-F, Lu Q, Xia Y, Li W. Learning knowledge from relevant webpage for opinion analysis. In: IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, WI-IAT ‘08. 2008, p. 307–13.
68.
Zurück zum Zitat Yang Y, Pedersen JO. A comparative study on feature selection in text categorization. In: ICML; 1997, p. 412–20. Yang Y, Pedersen JO. A comparative study on feature selection in text categorization. In: ICML; 1997, p. 412–20.
69.
Zurück zum Zitat Ye Q, Shi W, Li Y. Sentiment classification for movie reviews in Chinese by improved semantic oriented approach. In: Proceedings of the 39th annual Hawaii international conference on system sciences, HICSS’06. IEEE; 2006, p. 53b–53b. Ye Q, Shi W, Li Y. Sentiment classification for movie reviews in Chinese by improved semantic oriented approach. In: Proceedings of the 39th annual Hawaii international conference on system sciences, HICSS’06. IEEE; 2006, p. 53b–53b.
70.
Zurück zum Zitat Ye Q, Zhang Z, Law R. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl. 2009;36:6527–35.CrossRef Ye Q, Zhang Z, Law R. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl. 2009;36:6527–35.CrossRef
71.
Zurück zum Zitat Zagibalov T, Carroll J. Automatic seed word selection for unsupervised sentiment classification of Chinese text. In: Proceedings of the 22nd international conference on computational linguistics, vol. 1. Association for Computational Linguistics; 2008, p. 1073–80. Zagibalov T, Carroll J. Automatic seed word selection for unsupervised sentiment classification of Chinese text. In: Proceedings of the 22nd international conference on computational linguistics, vol. 1. Association for Computational Linguistics; 2008, p. 1073–80.
72.
Zurück zum Zitat Zhang Z-Q, Li Y-J, Ye Q, Law R. Sentiment classification for Chinese product reviews using an unsupervised Internet-based method. In: International conference on management science and engineering, 2008. ICMSE 2008. 15th Annual conference proceedings. IEEE; 2008, p. 3–9. Zhang Z-Q, Li Y-J, Ye Q, Law R. Sentiment classification for Chinese product reviews using an unsupervised Internet-based method. In: International conference on management science and engineering, 2008. ICMSE 2008. 15th Annual conference proceedings. IEEE; 2008, p. 3–9.
73.
Zurück zum Zitat Zhu S, Xu B, Zheng D, Zhao T. Chinese microblog sentiment analysis based on semi-supervised learning. In: Semantic web and web science. New York: Springer; 2013, p. 325–31. Zhu S, Xu B, Zheng D, Zhao T. Chinese microblog sentiment analysis based on semi-supervised learning. In: Semantic web and web science. New York: Springer; 2013, p. 325–31.
74.
Zurück zum Zitat Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102–7.CrossRef Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102–7.CrossRef
75.
Zurück zum Zitat Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Cham: Springer; 2015. ISBN 978-3-319-23654-4.CrossRef Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Cham: Springer; 2015. ISBN 978-3-319-23654-4.CrossRef
76.
Zurück zum Zitat Poria S, Gelbukh A, Cambria E, Das D, Bandyopadhyay S. Enriching SenticNet polarity scores through semi-supervised fuzzy clustering. In: Proceedings of ICDM, 2012, p. 709–716. Poria S, Gelbukh A, Cambria E, Das D, Bandyopadhyay S. Enriching SenticNet polarity scores through semi-supervised fuzzy clustering. In: Proceedings of ICDM, 2012, p. 709–716.
77.
Zurück zum Zitat Poria S, Gelbukh A, Cambria E, Yang P, Hussain A, Durrani T. Merging SenticNet and WordNet-affect emotion lists for sentiment analysis. In: Proceedings of ICSP, 2012, p. 1251–1255. Poria S, Gelbukh A, Cambria E, Yang P, Hussain A, Durrani T. Merging SenticNet and WordNet-affect emotion lists for sentiment analysis. In: Proceedings of ICSP, 2012, p. 1251–1255.
78.
Zurück zum Zitat Cambria E, Schuller B, Liu B, Wang H, Havasi C. Statistical approaches to concept-level sentiment analysis. IEEE Intell Syst. 2013;28(3):6–9.CrossRef Cambria E, Schuller B, Liu B, Wang H, Havasi C. Statistical approaches to concept-level sentiment analysis. IEEE Intell Syst. 2013;28(3):6–9.CrossRef
79.
Zurück zum Zitat Poria S, Cambria E, Howard N, Huang G-B, Hussain A. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing. 2016;174:50–9.CrossRef Poria S, Cambria E, Howard N, Huang G-B, Hussain A. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing. 2016;174:50–9.CrossRef
80.
Zurück zum Zitat Poria S, Cambria E, Hussain A, Huang G-B. Towards an intelligent framework for multimodal affective data analysis. Neural Netw. 2015;63:104–16.CrossRefPubMed Poria S, Cambria E, Hussain A, Huang G-B. Towards an intelligent framework for multimodal affective data analysis. Neural Netw. 2015;63:104–16.CrossRefPubMed
81.
Zurück zum Zitat Cambria E, Wang H, White B. Guest editorial: big social data analysis. Knowl Based Syst. 2014;69:1–2.CrossRef Cambria E, Wang H, White B. Guest editorial: big social data analysis. Knowl Based Syst. 2014;69:1–2.CrossRef
Metadaten
Titel
Erratum to: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques
verfasst von
Kia Dashtipour
Soujanya Poria
Amir Hussain
Erik Cambria
Ahmad Y. A. Hawalah
Alexander Gelbukh
Qiang Zhou
Publikationsdatum
01.08.2016
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 4/2016
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-016-9421-9

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