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Artificial Intelligence Review

Erschienen in:

07.02.2022

A survey on sentiment analysis methods, applications, and challenges

verfasst von: Mayur Wankhade, Annavarapu Chandra Sekhara Rao, Chaitanya Kulkarni

Erschienen in: Artificial Intelligence Review | Ausgabe 7/2022

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Abstract

The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

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Abid F, Alam M, Yasir M, Li C (2019) Sentiment analysis through recurrent variants latterly on convolutional neural network of twitter. Futur Gener Comput Syst 95:292–308CrossRef

Acheampong FA, Wenyu C, Nunoo-Mensah H (2020) Text-based emotion detection: advances, challenges, and opportunities. Eng Rep 2(7):e12189

Acheampong FA, Nunoo-Mensah H, Chen W (2021) Transformer models for text-based emotion detection: a review of BERT-based approaches. Artif Intell Rev 54:5789–5829CrossRef

Adomavicius G, Kwon Y (2011) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans Knowl Data Eng 24(5):896–911CrossRef

Ahmad S, Asghar MZ, Alotaibi FM, Awan I (2019) Detection and classification of social media-based extremist affiliations using sentiment analysis techniques. Hum Centric Comput Inf Sci 9(1):1–23

Ahmad SR, Bakar AA, Yaakub MR (2019) A review of feature selection techniques in sentiment analysis. Intell Data Anal 23(1):159–189CrossRef

Akhtar MS, Ekbal A, Cambria E (2020) How intense are you? predicting intensities of emotions and sentiments using stacked ensemble [application notes]. IEEE Comput Intell Mag 15(1):64–75CrossRef

Akhtar N, Zubair N, Kumar A, Ahmad T (2017) Aspect based sentiment oriented summarization of hotel reviews. Procedia Comput Sci 115:563–571CrossRef

Al Amrani Y, Lazaar M, El Kadiri KE (2018) Random forest and support vector machine based hybrid approach to sentiment analysis. Procedia Comput Sci 127:511–520CrossRef

Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B (2018) Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. J Comput Sci 27:386–393CrossRef

Alhumoud SO, Al Wazrah AA (2021) Arabic sentiment analysis using recurrent neural networks: a review. Artif Intell Rev 55:707–748CrossRef

Ali SM, Noorian Z, Bagheri E, Ding C, Al-Obeidat F (2020) Topic and sentiment aware microblog summarization for twitter. J Intell Inf Syst 54(1):129–156CrossRef

Annett M, Kondrak G (2008) A comparison of sentiment analysis techniques: Polarizing movie blogs. In: Conference of the Canadian Society for Computational Studies of Intelligence. Springer, pp 25–35

Arora A, Chakraborty P, Bhatia M, Mittal P (2021) Role of emotion in excessive use of twitter during COVID-19 imposed lockdown in India. J Technol Behav Sci 6(2):370–377CrossRef

Baashar Y, Alhussian H, Patel A, Alkawsi G, Alzahrani AI, Alfarraj O, Hayder G (2020) Customer relationship management systems (CRMS) in the healthcare environment: a systematic literature review. Comput Stand Interfaces 71:103442CrossRef

Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Lrec 2010:2200–2204

Bai X (2011) Predicting consumer sentiments from online text. Decis Support Syst 50(4):732–742CrossRef

Bai X, Liu P, Zhang Y (2020) Investigating typed syntactic dependencies for targeted sentiment classification using graph attention neural network. IEEE/ACM Trans Audio Speech Lang Process 29:503–514CrossRef

Balaji T, Annavarapu CSR, Bablani A (2021) Machine learning algorithms for social media analysis: a survey. Comput Sci Rev 40:100395CrossRef

Bandara K, Bergmeir C, Smyl S (2020) Forecasting across time series databases using recurrent neural networks on groups of similar series: a clustering approach. Expert Syst Appl 140:112896CrossRef

Bartusiak R, Augustyniak L, Kajdanowicz T, Kazienko P (2015) Sentiment analysis for polish using transfer learning approach. In: 2015 second european network intelligence conference. IEEE, pp 53–59

Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR (2021) ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur Gener Comput Syst 115:279–294CrossRef

Behdenna S, Barigou F, Belalem G (2018) Document level sentiment analysis: a survey. EAI Endorsed Trans Context-aware Syst Appl 4(13):e2

Bergsma S, McNamee P, Bagdouri M, Fink C, Wilson T (2012) Language identification for creating language-specific twitter collections. In: Proceedings of the second workshop on language in social media, pp 65–74

Bhaskar J, Sruthi K, Nedungadi P (2015) Hybrid approach for emotion classification of audio conversation based on text and speech mining. Procedia Comput Sci 46:635–643CrossRef

Bhatia P, Ji Y, Eisenstein J (2015) Better document-level sentiment analysis from rst discourse parsing. arXiv preprint arXiv:150901599

Birjali M, Kasri M, Beni-Hssane A (2021) A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl-Based Syst 226:107134CrossRef

Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146CrossRef

Bordes A, Glorot X, Weston J, Bengio Y (2014) A semantic matching energy function for learning with multi-relational data. Mach Learn 94(2):233–259MathSciNetMATHCrossRef

Borg A, Boldt M (2020) Using VADER sentiment and SVM for predicting customer response sentiment. Expert Syst Appl 162:113746CrossRef

Bose R, Dey RK, Roy S, Sarddar D (2020) Sentiment analysis on online product reviews. In: Information and communication technology for sustainable development. Springer, pp 559–569

Buder J, Rabl L, Feiks M, Badermann M, Zurstiege G (2021) Does negatively toned language use on social media lead to attitude polarization? Comput Hum Behav 116:106663CrossRef

Calders T, Verwer S (2010) Three naive bayes approaches for discrimination-free classification. Data Min Knowl Disc 21(2):277–292MathSciNetCrossRef

Cambria E, Das D, Bandyopadhyay S, Feraco A (2017) Affective computing and sentiment analysis. In: A practical guide to sentiment analysis. Springer, pp 1–10

Cambria E, Li Y, Xing FZ, Poria S, Kwok K (2020) Senticnet 6: ensemble application of symbolic and subsymbolic ai for sentiment analysis. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 105–114

Cao Q, Duan W, Gan Q (2011) Exploring determinants of voting for the “helpfulness’’ of online user reviews: a text mining approach. Decis Support Syst 50(2):511–521CrossRef

Cao Y, Zhang P, Xiong A (2015) Sentiment analysis based on expanded aspect and polarity-ambiguous word lexicon. Int J Adv Comput Sci Appl 6(2):97–103

Carvalho J, Plastino A (2021) On the evaluation and combination of state-of-the-art features in twitter sentiment analysis. Artif Intell Rev 54(3):1887–1936CrossRef

Castro S, Hazarika D, Pérez-Rosas V, Zimmermann R, Mihalcea R, Poria S (2019) Towards multimodal sarcasm detection (an _obviously_ perfect paper). arXiv preprint arXiv:190601815

Celik Y, Talo M, Yildirim O, Karabatak M, Acharya UR (2020) Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recogn Lett 133:232–239CrossRef

Chang JR, Liang HY, Chen LS, Chang CW (2020) Novel feature selection approaches for improving the performance of sentiment classification. J Ambient Intell Humaniz Comput pp 1–14

Chatterjere A, Guptha V, Chopra P, Das A (2020) Minority positive sampling for switching points-an anecdote for the code-mixing language modeling. In: Proceedings of the 12th language resources and evaluation conference, pp 6228–6236

Chen CC, Tseng YD (2011) Quality evaluation of product reviews using an information quality framework. Decis Support Syst 50(4):755–768CrossRef

Chen X, Wang Y, Liu Q (2017) Visual and textual sentiment analysis using deep fusion convolutional neural networks. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 1557–1561

Cheng Y, Yao L, Xiang G, Zhang G, Tang T, Zhong L (2020) Text sentiment orientation analysis based on multi-channel CNN and bidirectional GRU with attention mechanism. IEEE Access 8:134964–134975CrossRef

Chetviorkin I, Loukachevitch N (2012) Extraction of Russian sentiment lexicon for product meta-domain. In: Proceedings of COLING 2012, pp 593–610

Chiew KL, Tan CL, Wong K, Yong KS, Tiong WK (2019) A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf Sci 484:153–166CrossRef

Cho H, Lee H (2019) Biomedical named entity recognition using deep neural networks with contextual information. BMC Bioinform 20(1):1–11CrossRef

Chunping O, Wen Z, Ying Y, Zhiming L, Xiaohua Y (2014) Topic sentiment analysis in Chinese news. Int J Multimed Ubiquitous Eng 9(11):385–396CrossRef

Clark EM, James T, Jones CA, Alapati A, Ukandu P, Danforth CM, Dodds PS (2018) A sentiment analysis of breast cancer treatment experiences and healthcare perceptions across twitter. arXiv preprint arXiv:180509959

Cortis K, Davis B (2021) Over a decade of social opinion mining: a systematic review. Artif Intell Rev 54:4873–4965CrossRef

Crawford M, Khoshgoftaar TM, Prusa JD, Richter AN, Al Najada H (2015) Survey of review spam detection using machine learning techniques. J Big Data 2(1):1–24CrossRef

Das H, Naik B, Behera H (2020) A jaya algorithm based wrapper method for optimal feature selection in supervised classification. J King Saud Univ Comput Inf Sci

Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th international conference on World Wide Web, pp 519–528

Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805

Donkers T, Loepp B, Ziegler J (2017) Sequential user-based recurrent neural network recommendations. In: Proceedings of the eleventh ACM conference on recommender systems, pp 152–160

Dragoni M, Petrucci G (2017) A neural word embeddings approach for multi-domain sentiment analysis. IEEE Trans Affect Comput 8(4):457–470CrossRef

Duric A, Song F (2012) Feature selection for sentiment analysis based on content and syntax models. Decis Support Syst 53(4):704–711CrossRef

Ebadi A, Xi P, Tremblay S, Spencer B, Pall R, Wong A (2021) Understanding the temporal evolution of covid-19 research through machine learning and natural language processing. Scientometrics 126(1):725–739CrossRef

Ebrahimi M, Yazdavar AH, Sheth A (2017) Challenges of sentiment analysis for dynamic events. IEEE Intell Syst 32(5):70–75CrossRef

Eke CI, Norman AA, Shuib L, Nweke HF (2020) Sarcasm identification in textual data: systematic review, research challenges and open directions. Artif Intell Rev 53(6):4215–4258CrossRef

El-Din DM (2016) Enhancement bag-of-words model for solving the challenges of sentiment analysis. Int J Adv Comput Sci Appl 7(1):244–252

Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of maxent for ecologists. Divers Distrib 17(1):43–57CrossRef

Ethayarajh K (2019) How contextual are contextualized word representations? Comparing the geometry of BERT, ELMO, and GPT-2 embeddings. arXiv preprint arXiv:190900512

Fan TK, Chang CH (2011) Blogger-centric contextual advertising. Expert Syst Appl 38(3):1777–1788CrossRef

Fang Z, Zhang Q, Tang X, Wang A, Baron C (2020) An implicit opinion analysis model based on feature-based implicit opinion patterns. Artif Intell Rev 53(6):4547–4574CrossRef

Ferrari A, Esuli A (2019) An NLP approach for cross-domain ambiguity detection in requirements engineering. Autom Softw Eng 26(3):559–598CrossRef

Filatova E (2012) Irony and sarcasm: corpus generation and analysis using crowdsourcing. In: Lrec, Citeseer, pp 392–398

Flek L (2020) Returning the N to NLP: towards contextually personalized classification models. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 7828–7838

Flekova L, Preoţiuc-Pietro D, Ruppert E (2015) Analysing domain suitability of a sentiment lexicon by identifying distributionally bipolar words. In: Proceedings of the 6th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 77–84

Fredriksen-Goldsen KI, Kim HJ (2017) The science of conducting research with LGBT older adults-an introduction to aging with pride: National health, aging, and sexuality/gender study (NHAS)

Gao Z, Feng A, Song X, Wu X (2019) Target-dependent sentiment classification with BERT. IEEE Access 7:154290–154299CrossRef

George DR, Rovniak LS, Kraschnewski JL (2013) Dangers and opportunities for social media in medicine. Clin Obstet Gynecol 56(3)

Ghazi D, Inkpen D, Szpakowicz S (2015) Detecting emotion stimuli in emotion-bearing sentences. In: International conference on intelligent text processing and computational linguistics. Springer, pp 152–165

Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3–4):325–338MathSciNetMATHCrossRef

Hailong Z, Wenyan G, Bo J (2014) Machine learning and lexicon based methods for sentiment classification: a survey. In: 2014 11th web information system and application conference. IEEE, pp 262–265

Hajek P, Barushka A, Munk M (2020) Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Comput Appl 32(23):17259–17274CrossRef

Hamdan H, Bellot P, Bechet F (2015) Lsislif: Crf and logistic regression for opinion target extraction and sentiment polarity analysis. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 753–758

Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y (2021) Transformer in transformer. arXiv preprint arXiv:210300112

Han T, Liu C, Yang W, Jiang D (2019) A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowl-Based Syst 165:474–487CrossRef

Hangya V, Farkas R (2017) A comparative empirical study on social media sentiment analysis over various genres and languages. Artif Intell Rev 47(4):485–505CrossRef

Hassonah MA, Al-Sayyed R, Rodan A, Ala’M AZ, Aljarah I, Faris H (2020) An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on twitter. Knowl-Based Syst 192:105353CrossRef

Heerschop B, van Iterson P, Hogenboom A, Frasincar F, Kaymak U (2011) Accounting for negation in sentiment analysis. In: 11th Dutch-Belgian information retrieval workshop (DIR 2011), Citeseer, pp 38–39

Hershcovich D, Donatelli L (2021) It’s the meaning that counts: the state of the art in NLP and semantics. KI-Künstliche Intelligenz pp 1–16

Ho C, Murad MAA, Doraisamy S, Kadir RA (2014) Extracting lexical and phrasal paraphrases: a review of the literature. Artif Intell Rev 42(4):851–894CrossRef

Hssina B, Merbouha A, Ezzikouri H, Erritali M (2014) A comparative study of decision tree id3 and c4. 5. Int J Adv Comput Sci Appl 4(2):13–19

Hu N, Bose I, Koh NS, Liu L (2012) Manipulation of online reviews: an analysis of ratings, readability, and sentiments. Decis Support Syst 52(3):674–684CrossRef

Hu X, Tang J, Gao H, Liu H (2014) Social spammer detection with sentiment information. In: 2014 IEEE international conference on data mining. IEEE, pp 180–189

Hunter ST, Cushenbery L, Friedrich T (2012) Hiring an innovative workforce: a necessary yet uniquely challenging endeavor. Hum Resour Manag Rev 22(4):303–322

Hussein DMEDM (2018) A survey on sentiment analysis challenges. J King Saud Univ Eng Sci 30(4):330–338

Imani MB, Keyvanpour MR, Azmi R (2013) A novel embedded feature selection method: a comparative study in the application of text categorization. Appl Artif Intell 27(5):408–427CrossRef

Jain PK, Pamula R, Ansari S, Sharma D, Maddala L (2019) Airline recommendation prediction using customer generated feedback data. In: 2019 4th international conference on information systems and computer networks (ISCON). IEEE, pp 376–379

Jain PK, Pamula R, Ansari S (2021) A supervised machine learning approach for the credibility assessment of user-generated content. Wirel Pers Commun 118(4):2469–2485CrossRef

Jain PK, Pamula R, Srivastava G (2021) A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Comput Sci Rev 41:100413CrossRef

Jain PK, Pamula R, Yekun EA (2021c) A multi-label ensemble predicting model to service recommendation from social media contents. J Supercomput 1–18

Jain PK, Quamer W, Pamula R, Saravanan V (2021d) Spsan: sparse self-attentive network-based aspect-aware model for sentiment analysis. J Ambient Intell Humaniz Comput 1–18

Jain PK, Saravanan V, Pamula R (2021) A hybrid CNN-LSTM: a deep learning approach for consumer sentiment analysis using qualitative user-generated contents. Trans Asian Low-Resour Lang Inf Process 20(5):1–15CrossRef

Jain PK, Yekun EA, Pamula R, Srivastava G (2021) Consumer recommendation prediction in online reviews using cuckoo optimized machine learning models. Comput Electr Eng 95:107397CrossRef

Janjua F, Masood A, Abbas H, Rashid I, Khan MMZM (2021) Textual analysis of traitor-based dataset through semi supervised machine learning. Futur Gener Comput Syst 125:652–660CrossRef

Jiménez-Zafra SM, Martín-Valdivia MT, Molina-González MD, Ureña-López LA (2019) How do we talk about doctors and drugs? sentiment analysis in forums expressing opinions for medical domain. Artif Intell Med 93:50–57CrossRef

Juraska J, Walker M (2021) Attention is indeed all you need: semantically attention-guided decoding for data-to-text nlg. arXiv preprint arXiv:210907043

Kaity M, Balakrishnan V (2020) Sentiment lexicons and non-english languages: a survey. Knowl Inf Syst 1–36

Kamal A (2013) Subjectivity classification using machine learning techniques for mining feature-opinion pairs from web opinion sources. arXiv preprint arXiv:13126962

Kanapala A, Pal S, Pamula R (2019) Text summarization from legal documents: a survey. Artif Intell Rev 51(3):371–402CrossRef

Kang H, Yoo SJ, Han D (2012) Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst Appl 39(5):6000–6010CrossRef

Kasmuri E, Basiron H (2017) Subjectivity analysis in opinion mining—a systematic literature review. Int J Adv Soft Comput Appl 9(3):133–159

Kaufmann M (2012) Jmaxalign: a maximum entropy parallel sentence alignment tool. In: Proceedings of COLING 2012: demonstration papers, pp 277–288

Khairnar J, Kinikar M (2013) Machine learning algorithms for opinion mining and sentiment classification. Int J Sci Res Publ 3(6):1–6

Khan MT, Durrani M, Ali A, Inayat I, Khalid S, Khan KH (2016) Sentiment analysis and the complex natural language. Complex Adapt Syst Model 4(1):1–19CrossRef

Kiritchenko S, Zhu X, Mohammad SM (2014) Sentiment analysis of short informal texts. J Artif Intell Res 50:723–762CrossRef

Kitaev N, Klein D (2018) Constituency parsing with a self-attentive encoder. arXiv preprint arXiv:180501052

Kolchyna O, Souza TT, Treleaven P, Aste T (2015) Twitter sentiment analysis: Lexicon method, machine learning method and their combination. arXiv preprint arXiv:150700955

Korkontzelos I, Nikfarjam A, Shardlow M, Sarker A, Ananiadou S, Gonzalez GH (2016) Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. J Biomed Inform 62:148–158CrossRef

Kosamkar V, Chaudhari SS (2013) Improved intrusion detection system using c4. 5 decision tree and support vector machine. PhD diss, Doctoral dissertation, Mumbai University

Kraaijeveld O, De Smedt J (2020) The predictive power of public twitter sentiment for forecasting cryptocurrency prices. J Int Finan Markets Inst Money 65:101188CrossRef

Kumar A, Garg G (2020) Systematic literature review on context-based sentiment analysis in social multimedia. Multimed Tools Appl 79(21):15349–15380CrossRef

Kumar A, Teeja MS (2012) Sentiment analysis: a perspective on its past, present and future. Int J Intell Syst Appl 4(10):1

Kumar KN, Uma V (2021) Intelligent sentinet-based lexicon for context-aware sentiment analysis: optimized neural network for sentiment classification on social media. J Supercomput 77:12801–12825CrossRef

Kumar S, Yadava M, Roy PP (2019) Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Inf Fusion 52:41–52CrossRef

Lakkaraju H, Socher R, Manning C (2014) Aspect specific sentiment analysis using hierarchical deep learning. In: NIPS Workshop on deep learning and representation learning, pp 1–9

Lal YK, Kumar V, Dhar M, Shrivastava M, Koehn P (2019) De-mixing sentiment from code-mixed text. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp 371–377

Lapponi E, Read J, Øvrelid L (2012) Representing and resolving negation for sentiment analysis. In: 2012 IEEE 12th international conference on data mining workshops. IEEE, pp 687–692

Lata K, Singh P, Dutta K (2020) A comprehensive review on feature set used for anaphora resolution. Artif Intell Rev 54:2917–3006CrossRef

Levashina J, Hartwell CJ, Morgeson FP, Campion MA (2014) The structured employment interview: narrative and quantitative review of the research literature. Pers Psychol 67(1):241–293CrossRef

Li F, Wang W, Xu J, Yi J, Wang Q (2019) Comparative study on vulnerability assessment for urban buried gas pipeline network based on SVM and ANN methods. Process Saf Environ Prot 122:23–32CrossRef

Li X, Bing L, Zhang W, Lam W (2019b) Exploiting BERT for end-to-end aspect-based sentiment analysis. arXiv preprint arXiv:191000883

Li YM, Li TY (2013) Deriving market intelligence from microblogs. Decis Support Syst 55(1):206–217CrossRef

Ligthart A, Catal C, Tekinerdogan B (2021) Systematic reviews in sentiment analysis: a tertiary study. Artif Intell Rev 54:4997–5053CrossRef

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

Ling M, Chen Q, Sun Q, Jia Y (2020) Hybrid neural network for Sina Weibo sentiment analysis. IEEE Trans Comput Soc Syst 7(4):983–990CrossRef

Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167CrossRef

Liu B, Zhang L (2012) A survey of opinion mining and sentiment analysis. In: Aggarwal C, Zhai C (eds) Mining text data. Springer, Boston, pp 415–463CrossRef

Liu B et al (2010) Sentiment analysis and subjectivity. Handb Nat Lang Process 2(2010):627–666

Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:160505101

Lu B, Ott M, Cardie C, Tsou BK (2011) Multi-aspect sentiment analysis with topic models. In: 2011 IEEE 11th international conference on data mining workshops. IEEE, pp 81–88

Mackey TK, Miner A, Cuomo RE (2015) Exploring the e-cigarette e-commerce marketplace: identifying internet e-cigarette marketing characteristics and regulatory gaps. Drug Alcohol Depend 156:97–103CrossRef

Majumder N, Hazarika D, Gelbukh A, Cambria E, Poria S (2018) Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowl-Based Syst 161:124–133CrossRef

Maks I, Vossen P (2012) A lexicon model for deep sentiment analysis and opinion mining applications. Decis Support Syst 53(4):680–688CrossRef

McDuff D, El Kaliouby R, Cohn JF, Picard RW (2014) Predicting ad liking and purchase intent: large-scale analysis of facial responses to ads. IEEE Trans Affect Comput 6(3):223–235CrossRef

Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113CrossRef

Mendon S, Dutta P, Behl A, Lessmann S (2021) A hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters. Inf Syst Front 23:1145–1168CrossRef

Meng J, Long Y, Yu Y, Zhao D, Liu S (2019) Cross-domain text sentiment analysis based on cnn_ft method. Information 10(5):162CrossRef

Mezquita Y, Alonso RS, Casado-Vara R, Prieto J, Corchado JM (2020) A review of k-nn algorithm based on classical and quantum machine learning. In: International symposium on distributed computing and artificial intelligence. Springer, pp 189–198

Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781

Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19(6):1236–1246CrossRef

Mite-Baidal K, Delgado-Vera C, Solís-Avilés E, Espinoza AH, Ortiz-Zambrano J, Varela-Tapia E (2018) Sentiment analysis in education domain: a systematic literature review. In: International conference on technologies and innovation. Springer, pp 285–297

Mohammad SM (2017) Challenges in sentiment analysis. In: A practical guide to sentiment analysis. Springer, pp 61–83

Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621–633CrossRef

Moreo A, Romero M, Castro J, Zurita JM (2012) Lexicon-based comments-oriented news sentiment analyzer system. Expert Syst Appl 39(10):9166–9180CrossRef

Mowlaei ME, Abadeh MS, Keshavarz H (2020) Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Syst Appl 148:113234CrossRef

Mukherjee A, Venkataraman V, Liu B, Glance N (2013) What yelp fake review filter might be doing? In: Proceedings of the international AAAI conference on web and social media, vol 7

Naseem U, Razzak I, Musial K, Imran M (2020) Transformer based deep intelligent contextual embedding for twitter sentiment analysis. Futur Gener Comput Syst 113:58–69CrossRef

Ortigosa-Hernández J, Rodríguez JD, Alzate L, Lucania M, Inza I, Lozano JA (2012) Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing 92:98–115CrossRef

Oueslati O, Cambria E, HajHmida MB, Ounelli H (2020) A review of sentiment analysis research in Arabic language. Futur Gener Comput Syst 112:408–430CrossRef

Paré DJ (2003) Does this site deliver? B2B e-commerce services for developing countries. Inf Soc 19(2):123–134CrossRef

Park HW, Park S, Chong M (2020) Conversations and medical news frames on twitter: infodemiological study on covid-19 in South Korea. J Med Internet Res 22(5):e18897CrossRef

Park S, Kim Y (2016) Building thesaurus lexicon using dictionary-based approach for sentiment classification. In: 2016 IEEE 14th international conference on software engineering research, management and applications (SERA), pp 39–44, https://doi.org/10.1109/SERA.2016.7516126

Parvin SA, Sumathi M, Mohan C (2021) Challenges of sentiment analysis-a survey. In: 2021 5th International conference on trends in electronics and informatics (ICOEI). IEEE, pp 781–786

Patel HH, Prajapati P (2018) Study and analysis of decision tree based classification algorithms. Int J Comput Sci Eng 6(10):74–78

Patil N, Lathi R, Chitre V (2012) Customer card classification based on c5. 0 & cart algorithms. Int J Eng Res Appl 2(4):164–167

Peng M, Zhang Q, Jiang Yg, Huang XJ (2018) Cross-domain sentiment classification with target domain specific information. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 2505–2513

Peng Y, Yan S, Lu Z (2019) Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. arXiv preprint arXiv:190605474

Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

Pham TH, Le-Hong P (2017) End-to-end recurrent neural network models for vietnamese named entity recognition: word-level vs. character-level. In: International conference of the Pacific Association for Computational Linguistics. Springer, pp 219–232

Piryani R, Madhavi D, Singh VK (2017) Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Inf Process Manag 53(1):122–150CrossRef

Plank B, Søgaard A, Goldberg Y (2016) Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. arXiv preprint arXiv:160405529

Poria S, Cambria E, Winterstein G, Huang GB (2014) Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl-Based Syst 69:45–63CrossRef

Poria S, Chaturvedi I, Cambria E, Hussain A (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: 2016 IEEE 16th international conference on data mining (ICDM). IEEE, pp 439–448

Poria S, Cambria E, Hazarika D, Mazumder N, Zadeh A, Morency LP (2017) Multi-level multiple attentions for contextual multimodal sentiment analysis. In: 2017 IEEE international conference on data mining (ICDM). IEEE, pp 1033–1038

Poria S, Hussain A, Cambria E (2018a) Combining textual clues with audio-visual information for multimodal sentiment analysis. In: Multimodal sentiment analysis. Springer, pp 153–178

Poria S, Majumder N, Hazarika D, Cambria E, Gelbukh A, Hussain A (2018) Multimodal sentiment analysis: addressing key issues and setting up the baselines. IEEE Intell Syst 33(6):17–25CrossRef

Poria S, Hazarika D, Majumder N, Mihalcea R (2020) Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research. IEEE Trans Affect Comput

Pravalika A, Oza V, Meghana N, Kamath SS (2017) Domain-specific sentiment analysis approaches for code-mixed social network data. In: 2017 8th international conference on computing, communication and networking technologies (ICCCNT). IEEE, pp 1–6

Qiu G, He X, Zhang F, Shi Y, Bu J, Chen C (2010) DASA: dissatisfaction-oriented advertising based on sentiment analysis. Expert Syst Appl 37(9):6182–6191CrossRef

Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier, Amsterdam

Rana TA, Cheah YN (2016) Aspect extraction in sentiment analysis: comparative analysis and survey. Artif Intell Rev 46(4):459–483CrossRef

Rao D, Ravichandran D (2009) Semi-supervised polarity lexicon induction. In: Proceedings of the 12th conference of the European chapter of the ACL (EACL 2009), pp 675–682

Rao G, Huang W, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49–57CrossRef

Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst 89:14–46CrossRef

Razon A, Barnden J (2015) A new approach to automated text readability classification based on concept indexing with integrated part-of-speech n-gram features. In: Proceedings of the international conference recent advances in natural language processing, pp 521–528

Remus R (2013) Modeling and representing negation in data-driven machine learning-based sentiment analysis. In: ESSEM@ AI* IA, pp 22–33

Revathy R, Lawrance R (2017) Comparative analysis of c4. 5 and c5. 0 algorithms on crop pest data. Int J Innovative Res Comput Commun Eng 5(1):50–58

Ritter A, Etzioni O, Clark S (2012) Open domain event extraction from twitter. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1104–1112

Rizos G, Hemker K, Schuller B (2019) Augment to prevent: short-text data augmentation in deep learning for hate-speech classification. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 991–1000

Rognone L, Hyde S, Zhang SS (2020) News sentiment in the cryptocurrency market: an empirical comparison with forex. Int Rev Financ Anal 69:101462CrossRef

Ruffer N, Knitza J, Krusche M (2020) # Covid4Rheum: an analytical twitter study in the time of the COVID-19 pandemic. Rheumatol Int 40(12):2031–2037CrossRef

Rui H, Liu Y, Whinston A (2013) Whose and what chatter matters? The effect of tweets on movie sales. Decis Support Syst 55(4):863–870CrossRef

Salah Z, Al-Ghuwairi ARF, Baarah A, Aloqaily A, Qadoumi B, Alhayek M, Alhijawi B (2019) A systematic review on opinion mining and sentiment analysis in social media. Int J Bus Inf Syst 31(4):530–554

Sánchez-Rada JF, Iglesias CA (2019) Social context in sentiment analysis: formal definition, overview of current trends and framework for comparison. Inf Fusion 52:344–356CrossRef

Sann R, Lai PC (2020) Understanding homophily of service failure within the hotel guest cycle: applying NLP-aspect-based sentiment analysis to the hospitality industry. Int J Hosp Manag 91:102678CrossRef

Saunders D (2021) Domain adaptation for neural machine translation. PhD thesis, University of Cambridge

Schouten K, Frasincar F (2015) Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng 28(3):813–830CrossRef

Sharma A, Lyons J, Dehzangi A, Paliwal KK (2013) A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition. J Theor Biol 320:41–46MathSciNetMATHCrossRef

Shayaa S, Jaafar NI, Bahri S, Sulaiman A, Wai PS, Chung YW, Piprani AZ, Al-Garadi MA (2018) Sentiment analysis of big data: methods, applications, and open challenges. IEEE Access 6:37807–37827CrossRef

Singh JP, Irani S, Rana NP, Dwivedi YK, Saumya S, Roy PK (2017) Predicting the “helpfulness’’ of online consumer reviews. J Bus Res 70:346–355CrossRef

Singh K, Sen I, Kumaraguru P (2018) A twitter corpus for Hindi-English code mixed POS tagging. In: Proceedings of the sixth international workshop on natural language processing for social media, pp 12–17

Singh M, Jakhar AK, Pandey S (2021) Sentiment analysis on the impact of coronavirus in social life using the BERT model. Soc Netw Anal Min 11(1):1–11CrossRef

Singh RK, Sachan MK, Patel R (2021) 360 degree view of cross-domain opinion classification: a survey. Artif Intell Rev 54(2):1385–1506CrossRef

Singh S, Gupta P (2014) Comparative study id3, cart and c4. 5 decision tree algorithm: a survey. Int J Adv Inf Sci Technol 27(27):97–103

Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C (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

Soleymani M, Garcia D, Jou B, Schuller B, Chang SF, Pantic M (2017) A survey of multimodal sentiment analysis. Image Vis Comput 65:3–14CrossRef

Stappen L, Schuller B, Lefter I, Cambria E, Kompatsiaris I (2020) Summary of MuSe 2020: multimodal sentiment analysis, emotion-target engagement and trustworthiness detection in real-life media. In: Proceedings of the 28th ACM international conference on multimedia, pp 4769–4770

Straka M, Hajic J, Straková J (2016) UDPipe: trainable pipeline for processing CoNLL-U files performing tokenization, morphological analysis, pos tagging and parsing. In: Proceedings of the tenth international conference on language resources and evaluation (LREC’16), pp 4290–4297

Subhashini L, Li Y, Zhang J, Atukorale AS, Wu Y (2021) Mining and classifying customer reviews: a survey. Artif Intell Rev 54:6343–6389CrossRef

Sun C, Huang L, Qiu X (2019) Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:190309588

Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307CrossRef

Thet TT, Na JC, Khoo CS (2010) Aspect-based sentiment analysis of movie reviews on discussion boards. J Inf Sci 36(6):823–848CrossRef

Tian Y, Galery T, Dulcinati G, Molimpakis E, Sun C (2017) Facebook sentiment: reactions and emojis. In: Proceedings of the fifth international workshop on natural language processing for social media, pp 11–16

Tran T, Ba H, Huynh VN (2019) Measuring hotel review sentiment: an aspect-based sentiment analysis approach. In: International symposium on integrated uncertainty in knowledge modelling and decision making. Springer, pp 393–405

Tripathy A, Agrawal A, Rath SK (2015) Classification of sentimental reviews using machine learning techniques. Procedia Comput Sci 57:821–829CrossRef

Tubishat M, Idris N, Abushariah MA (2018) Implicit aspect extraction in sentiment analysis: review, taxonomy, oppportunities, and open challenges. Inf Process Manag 54(4):545–563CrossRef

Turney PD, Littman ML (2003) Measuring praise and criticism: inference of semantic orientation from association. ACM Trans Inf Syst 21(4):315–346CrossRef

Uysal AK, Murphey YL (2017) Sentiment classification: feature selection based approaches versus deep learning. In: 2017 IEEE international conference on computer and information technology (CIT). IEEE, pp 23–30

Valdivia A, Luzíón MV, Herrera F (2017) Neutrality in the sentiment analysis problem based on fuzzy majority. In: 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, pp 1–6

Valdivia A, Luzón MV, Cambria E, Herrera F (2018) Consensus vote models for detecting and filtering neutrality in sentiment analysis. Inf Fusion 44:126–135CrossRef

Valencia F, Gómez-Espinosa A, Valdés-Aguirre B (2019) Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy 21(6):589CrossRef

Van de Camp M, Van den Bosch A (2012) The socialist network. Decis Support Syst 53(4):761–769CrossRef

Varelas G, Voutsakis E, Raftopoulou P, Petrakis EG, Milios EE (2005) Semantic similarity methods in wordnet and their application to information retrieval on the web. In: Proceedings of the 7th annual ACM international workshop on Web information and data management, pp 10–16

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv preprint arXiv:170603762

Vateekul P, Koomsubha T (2016) A study of sentiment analysis using deep learning techniques on Thai twitter data. In: 2016 13th international joint conference on computer science and software engineering (JCSSE). IEEE, pp 1–6

Vechtomova O (2017) Disambiguating context-dependent polarity of words: an information retrieval approach. Inf Process Manag 53(5):1062–1079CrossRef

Venugopalan M, Gupta D (2015) Exploring sentiment analysis on twitter data. In: 2015 eighth international conference on contemporary computing (IC3). IEEE, pp 241–247

Vijay D, Bohra A, Singh V, Akhtar SS, Shrivastava M (2018) Corpus creation and emotion prediction for Hindi-English code-mixed social media text. In: Proceedings of the 2018 conference of the North American chapter of the Association for Computational Linguistics: student research workshop, pp 128–135

Wadawadagi R, Pagi V (2020) Sentiment analysis with deep neural networks: comparative study and performance assessment. Artif Intell Rev 53:6155–6195CrossRef

Wang G, Sun J, Ma J, Xu K, Gu J (2014) Sentiment classification: the contribution of ensemble learning. Decis Support Syst 57:77–93CrossRef

Wang Z, Ho SB, Cambria E (2020) Multi-level fine-scaled sentiment sensing with ambivalence handling. Int J Uncertain Fuzziness Knowl-Based Syst 28(04):683–697CrossRef

Wankhade M, Annavarapu CSR, Verma MK (2021) CBVoSD: context based vectors over sentiment domain ensemble model for review classification. J Supercomput 1–37

Weerasooriya T, Perera N, Liyanage S (2016) A method to extract essential keywords from a tweet using NLP tools. In: 2016 sixteenth international conference on advances in ICT for emerging regions (ICTer). IEEE, pp 29–34

Wilson T, Wiebe J, Hoffmann P (2009) Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput Linguist 35(3):399–433CrossRef

Wu D, Chi M (2017) Long short-term memory with quadratic connections in recursive neural networks for representing compositional semantics. IEEE Access 5:16077–16083CrossRef

Wu P, Li X, Shen S, He D (2020) Social media opinion summarization using emotion cognition and convolutional neural networks. Int J Inf Manag 51:101978CrossRef

Xia H, Yang Y, Pan X, Zhang Z, An W (2020) Sentiment analysis for online reviews using conditional random fields and support vector machines. Electron Commer Res 20(2):343–360CrossRef

Xia Y, Cambria E, Hussain A, Zhao H (2015) Word polarity disambiguation using Bayesian model and opinion-level features. Cognit Comput 7(3):369–380CrossRef

Xing FZ, Cambria E, Welsch RE (2018) Natural language based financial forecasting: a survey. Artif Intell Rev 50(1):49–73CrossRef

Yadav A, Vishwakarma DK (2020) Sentiment analysis using deep learning architectures: a review. Artif Intell Rev 53(6):4335–4385CrossRef

Yan-Yan Z, Bing Q, Ting L (2010) Integrating intra-and inter-document evidences for improving sentence sentiment classification. Acta Autom Sinica 36(10):1417–1425

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 (Volume 1: Long Papers), pp 325–335

Yaseen Q et al (2021) Spam email detection using deep learning techniques. Procedia Comput Sci 184:853–858CrossRef

Yousif A, Niu Z, Tarus JK, Ahmad A (2019) A survey on sentiment analysis of scientific citations. Artif Intell Rev 52(3):1805–1838CrossRef

Yuan Z, Wu S, Wu F, Liu J, Huang Y (2018) Domain attention model for multi-domain sentiment classification. Knowl-Based Syst 155:1–10CrossRef

Yue L, Chen W, Li X, Zuo W, Yin M (2019) A survey of sentiment analysis in social media. Knowl Inf Syst 60(2):617–663CrossRef

Zhang Z, Wang L, Zou Y, Gan C (2018) The optimally designed dynamic memory networks for targeted sentiment classification. Neurocomputing 309:36–45CrossRef

Zhao W, Guan Z, Chen L, He X, Cai D, Wang B, Wang Q (2017) Weakly-supervised deep embedding for product review sentiment analysis. IEEE Trans Knowl Data Eng 30(1):185–197CrossRef

Zhao Y, Xu X, Wang M (2019) Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews. Int J Hosp Manag 76:111–121CrossRef

Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Synth Lect Artif Intell Mach Learn 3(1):1–130MATH

Zuo E, Zhao H, Chen B, Chen Q (2020) Context-specific heterogeneous graph convolutional network for implicit sentiment analysis. IEEE Access 8:37967–37975CrossRef

Zvarevashe K, Olugbara OO (2018) A framework for sentiment analysis with opinion mining of hotel reviews. In: 2018 Conference on information communications technology and society (ICTAS). IEEE, pp 1–4

Titel: A survey on sentiment analysis methods, applications, and challenges
verfasst von: Mayur Wankhade
Annavarapu Chandra Sekhara Rao
Chaitanya Kulkarni
Publikationsdatum: 07.02.2022
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 7/2022
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-022-10144-1

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