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A systematic study on the role of SentiWordNet in opinion mining

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Abstract

Sentiment lexicons (SL) (aka lexical resources) are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms. These lexicons play an important role in performing several different opinion mining tasks. The efficacy of the lexicon-based approaches in performing opinion mining (OM) tasks solely depends on selecting an appropriate opinion lexicon to analyze the text. Therefore, one has to explore the available sentiment lexicons and then select the most suitable resource. Among available resources, SentiWordNet (SWN) is the most widely used lexicon to perform tasks related to opinion mining. In SWN, each synset of WordNet is being assigned the three sentiment numerical scores; positive, negative and objective that are calculated using by a set of classifiers. In this paper, a detailed and comprehensive review of the work related to opinion mining using SentiWordNet is provided in a very distinctive way. This survey will be useful for the researchers contributing to the field of opinion mining. Following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to opinion mining tasks and subtasks (ii) it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels (word, sentences, document, aspect, clause, and concept levels) (iii) this state-of-art review covers each article in the following dimensions: the designated task performed, granularity level of the task completed, results obtained, and feature dimensions, and (iv) lastly it concludes the summary of the related articles according to the granularity levels, publishing years, related tasks (or subtasks), and types of classifiers used. In the end, major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

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References

  1. Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1–167

    Article  Google Scholar 

  2. Liu B, Zhang L. A survey of opinion mining and sentiment analysis. Mining Text Data. 3rd ed. Springer, 2012

  3. 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, 2002, 79–86

  4. Picard R W. Affective computing: from laughter to IEEE. IEEE Transactions on Affective Computing, 2010, 1(1): 11–17

    Article  Google Scholar 

  5. Missen M M S, Boughanem M, Cabanac G. Opinion mining: reviewed from word to document level. Social Network Analysis and Mining, 2013, 3(1): 107–125

    Article  Google Scholar 

  6. Liu B, Street S M. Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th International Conference on World Wide Web. 2005, 342–351

  7. Esuli A, Sebastiani F, Moruzzi V G. SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th International Conference on Language Resources and Evaluation. 2006, 417–422

  8. Strapparava C, Strapparava C, Valitutti A. WordNet-affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation. 2004, 1083–1086

  9. Chen L S, Liu C H, Chiu H J. A neural network based approach for sentiment classification in the blogosphere. Journal of Informetrics, 2011, 5(2): 313–322

    Article  Google Scholar 

  10. Singhal A. Modern information retrieval: a brief overview. IEEE Data Engineering Bulletin, 2011, 24(4): 35–43

    Google Scholar 

  11. Liu B. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. 2nd ed. Cambridge University Press, 2016

  12. Herrera F. Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence, 2008, 1(1): 27–46

    Article  Google Scholar 

  13. Deshmukh J S, Tripathy A K. Entropy based classifier for cross-domain opinion mining. Applied Computing and Informatics, 2018, 14(1): 55–64

    Article  Google Scholar 

  14. Cambria E, Hussain A. Sentic computing: techniques, tools, and applications. Springer Science & Business Media, 2012, 59(2): 557–577

    Google Scholar 

  15. Peng F, Schuurmans D. Combining naive bayes and n-gram language models for text classification. In: Proceeding of European Conference on Information Retrieval. 2003, 335–350

  16. Cambria E, Olsher D, Rajagopal D. SenticNet3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2014, 1515–1521

  17. Tang H, Tan S, Cheng X. A survey on sentiment detection of reviews. Expert Systems with Applications, 2009, 36(7): 10760–10773

    Article  Google Scholar 

  18. Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems, 2015, 89: 14–46

    Article  Google Scholar 

  19. Baccianella S, Esuli A, Sebastiani F. Sentiwordnet3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th International Conference on Language Resources and Evaluation. 2010, 2200–2204

  20. Strapparava C, Mihalcea R. Learning to identify emotions in text. In: Proceedings of the 2008 ACM Symposium on Applied Computing. 2008, 1556–1560

  21. Hamdan H, Bachet F, Bellot P. Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-blogging. In: Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics. 2013, 455–459

  22. Miller G A. WordNet: a lexical database. Communications of the ACM, 1995, 38(11): 39–41

    Article  Google Scholar 

  23. Stone P J, Bales R F, Namenwirth J Z, Ogilvie D M. The general inquirer: a computer system for content analysis and retrieval based on the sentence as a unit of information. Behavioral Science, 1962, 7(4): 484–498

    Article  Google Scholar 

  24. Stone P J, Hunt E B. A computer approach to content analysis: studies using the general inquirer system. In: Proceedings of the Spring Joint Computer Conference. 1963, 241–256

  25. Esuli A. Automatic generation of lexical resources for opinion mining model. Association for Computing Machinery, 2008, 42(2): 105–106

    Google Scholar 

  26. Jiang L, Yu M, Zhou M, Liu X, Zhao T. Target-dependent Twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 2011, 151–160

  27. Balazs J A, Velasquez J D. Opinion mining and information fusion: a survey. Information Fusion, 2016, 27: 95–110

    Article  Google Scholar 

  28. Khan F H, Bashir S, Qamar, U. TOM: Twitter opinion mining framework using hybrid classification scheme. Decision Support System, 2014, 57: 245–257

    Article  Google Scholar 

  29. Bakliwal A, Foster J, van der Puil J, O’Brien R, Tounsi L, Hughes M. Sentiment analysis of political tweets: towards an accurate classifier. In: Proceedings of NAACL Workshop on Language Analysis in Social Media. 2013, 49–58

  30. Kaur A, Gupta V. A survey on sentiment analysis and opinion mining techniques. Journal of Emerging Technologies in Web Intelligence, 2013, 5(4): 367–371

    Article  Google Scholar 

  31. Hall A. Archiving academic tweets: the digital backchannel as an ephemeral archive. Reconstruction: Studies in Contemporary Culture, 2016, 16(1): 12–14

    Google Scholar 

  32. Bao H, Li Q, Liao S S, Song S, Gao H. A new temporal and social PMF-based method to predict users’ interests in micro-blogging. Decision Support Systems, 2013, 55(3): 698–709

    Article  Google Scholar 

  33. Li W, Xu H. Text-based emotion classification using emotion cause extraction. Expert Systems with Applications, 2014, 41(4): 1742–1749

    Article  Google Scholar 

  34. Zhang K, Xie Y, Yang Y, Sun A, Liu H, Choudhary A. Incorporating conditional random fields and active learning to improve sentiment identification. Neural Networks, 2014, 58: 60–67

    Article  Google Scholar 

  35. Ortigosa A, Martin J M, Carro R M. Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 2014, 31: 527–541

    Article  Google Scholar 

  36. Falagas M E, Pitsouni E I, Malietzis G A, Pappas G. Comparison of PubMed, Scopus, Web of science, and Google scholar: strengths and weaknesses. The FASEB Journal, 2007, 22(2): 338–342

    Article  Google Scholar 

  37. Shelke N. Survey of techniques for opinion mining. International Journal of Computer Applications, 2012, 57(13): 30–35

    Google Scholar 

  38. Toutanova K, Klein D, Manning C D, Singer Y. Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. 2003, 173–180

  39. Chris D P. Another stemmer. In: Proceedings of the ACM SIGIR Forum. 1990, 56–61

  40. Derczynski L, Ritter A, Clark S, Bontcheva K. Twitter part-of-speech tagging for all: overcoming sparse and noisy data. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing RANLP. 2013, 198–206

  41. Porter M F. Snowball: a language for stemming algorithms. Then and Now, 2006, 40(3): 219–224

    Google Scholar 

  42. Feldman R, Sanger J. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. 3rd ed. Cambridge University Press. 2007

  43. Kreutzer J, Witte N. Opinion Mining Using SentiWordNet. 3rd ed. Uppsala University. 2013

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

    Article  Google Scholar 

  45. Na S H, Lee Y, Nam S H, Lee J H. Improving opinion retrieval based on query-specific sentiment lexicon. In: Proceedings of European Conference on Information Retrieval. 2009, 734–738

  46. Tsytsarau M, Palpanas T. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 2012, 24(3): 478–514

    Article  MATH  Google Scholar 

  47. Yadav V, Elchuri H. Serendio: simple and practical lexicon based approach to sentiment analysis. In: Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics. 2013, 543–548

  48. Dang Y, Zhang Y, Chen H. A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intelligent Systems, 2010, 25(4): 46–53

    Article  Google Scholar 

  49. Musto C, Semeraro G, Polignano M. A comparison of lexicon-based approaches for sentiment analysis of microblog. In: Proceedings of the 8th International Workshop on Information Filtering and Retrieval. 2014, 59–68

  50. Zhang M. A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2008, 411–418

  51. Goncalves P, Araujo M, Benevenuto F, Cha M. Comparing and combining sentiment analysis methods. In: Proceedings of the 1st ACM Conference on Online Social Networks. 2014, 27–38

  52. Ohana B. Opinion mining with the SentWordNet lexical resource. MSc Dissertation. Technological University Dublin, 2009

  53. Vohra S M. A comparative study of sentiment analysis techniques. Journal Jikrce, 2013, 2(2): 313–317

    Google Scholar 

  54. Ribeiro F N, Arajo M, Goncalves P, Andre Goncalves M, Benevenuto F. SentiBench — a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 2016, 5(1): 1–29

    Article  Google Scholar 

  55. Du X, Emebo O, Varde A, Tandon N, Chowdhury S N, Weikum G. Air quality assessment from social media and structured data: pollutants and health impacts in urban planning. In: Proceedings of the Data Engineering Workshops. 2016, 54–59

  56. Saif H, He Y, Fernandez M, Alani H. Contextual semantics for sentiment analysis of Twitter. Information Processing and Management, 2016, 52(1): 5–19

    Article  Google Scholar 

  57. Missen M M S, Coustaty M, Salamat N, Prasath V B S. SentiML++:an extension of the SentiML sentiment annotation scheme. New Review of Hypermedia and Multimedia, 2018, 24(1): 28–43

    Article  Google Scholar 

  58. Andrea Esuli F S. Determining the semantic orientation of terms through gloss classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. 2005, 617–624

  59. Wiebe J, Wilson T, Cardie C. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 2005, 39(2): 165–210

    Article  Google Scholar 

  60. Montejo-Ráez A, Martinez-Cámara E, Ureña-López L A. Random walk weighting over sentiWordNet for sentiment polarity detection on Twitter. In: Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis. 2012, 3–10

  61. Lovasz L. Random walks on graphs: a survey. Combinatorics, 1993, 2(1): 1–46

    MathSciNet  Google Scholar 

  62. Saggion H, Funk A. Interpreting SentiWordNet for opinion classification. In: Proceeding of the 7th Conference on International Language Resources and Evaluation. 2010, 1129–1133

  63. Amiri H, Chua T. Sentiment Classification Using the Meaning of Words. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence. 2012, 39–42

  64. Hung C, Lin H K. Using objective words in SentiWordNet to improve word-of-mouth sentiment classification. IEEE Intelligent Systems, 2013, 28(2): 47–54

    Article  Google Scholar 

  65. Hung C, Tsai C F, Huang H. Extracting word-of-mouth sentiments via SentiWordNet for document quality classification. Recent Patents on Computer Science, 2012, 5(2): 145–152

    Article  Google Scholar 

  66. Bollegala D, Weir D, Carroll J. Cross-domain sentiment classification using an automatically extracted sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(8): 1719–1731

    Article  Google Scholar 

  67. Blitzer J, Dredze M, Pereira F. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 2007, 440–447

  68. Lopez A, Veale T, Majumder P. Feature extraction from product reviews using feature similarity and polarity. Heterogeneous Computing Laboratory. UCD School of Computer Science and Informatics Technical Report UCD-CSI-2009. 2009

  69. Tofighy S, Fakhrahmad S M. A proposed scheme for sentiment analysis. Kybernetes, 2018, 5(47): 957–984

    Article  Google Scholar 

  70. Khan F H, Qamar U, Bashir S. A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet. Knowledge and Information Systems, 2017, 51(3): 851–872

    Article  Google Scholar 

  71. Neviarouskaya A. SentiFul: generating a reliable lexicon for sentiment analysis. In: Proceeding the 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops. 2009, 1–6

  72. Miguel P, Cardoso D, Villedo S, Roy A, Villedo S. Sentiment lexicon creation using continuous latent space and neural networks. In: Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2016, 37–42

  73. Mullen T, Collier N. Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. 2004, 412–418

  74. Tripathy A, Agrawal A, Rath S K. Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 2016, 57: 117–126

    Article  Google Scholar 

  75. OKeefe T, Koprinska I. Feature selection and weighting methods in sentiment analysis. In: Proceedings of the 14th Australasian Document Computing Symposium, Sydney. 2009, 67–74

  76. Denecke K. Using SentiWordNet for multilingual sentiment analysis. In: Proceedings of International Conference on Data Engineering. 2008, 507–512

  77. Lango M, Brzezinski D, Stefanowski J. PUT at SemEval-2016 Task 4: the ABC of Twitter sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation. 2016, 131–137

  78. Yang C, Bhattacharya S, Srinivasan P. Lexical and machine learning approaches toward online reputation management. In: Proceedings of CLEF Conference and Labs of the Evaluation Forum. 2012, 71–78

  79. Dodds P S, Harris K D, Kloumann I M, Bliss C A, Danforth C M. Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter. PLoS ONE, 2011, 6(12): 1–26

    Article  Google Scholar 

  80. Qu L, Toprak C, Jakob N, Gurevych I. Sentence level subjectivity and sentiment analysis experiments in NTCIR-7 MOAT challenge. In: Proceedings of the 7th NTCIR Workshop Meeting on Evaluation of Information Access Technologies:Information Retrieval, Question Answering, and Cross-Lingual Information Access. 2008, 210–217

  81. Balahur A, Steinberger R, Kabadjov M, Zavarella V, Van Der Goot E, Halkia M, Pouliquen B, Belyaeva J. Sentiment analysis in the news. In: Proceedings of the 7thInternational Conference on Language Resources and Evaluation. 1984, 293–295

  82. Balahur A, Steinberger R, Van Der Goot E, Pouliquen B, Kabadjov M. Opinion mining on newspaper quotations. In: Proceedings of 2009 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 2009, 523–526

  83. Mittal A, Goel A. Stock prediction using Twitter sentiment analysis. Stanford University, CS229. 2012

  84. Guerini M, Gatti L, Turchi M. Sentiment analysis: how to derive prior polarities from SentiWordNet. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013, 1259–1269

  85. Perez-Rosas V, Banea C, Mihalcea R. Learning sentiment lexicons in spanish. In: Proceedings of the 8th International Conference on Language Resources and Evaluation. 2012, 3077–3081

  86. Martin-Valdivia M T, Martinez-Camara E, Perea-Ortega J M, Urena-Lopez L A. Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches. Expert Systems with Applications, 2013, 10: 3934–3942

    Article  Google Scholar 

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

    Article  Google Scholar 

  88. Desmet B, Hoste V. Emotion detection in suicide notes. Expert Systems with Applications, 2013, 40(16): 6351–6358

    Article  Google Scholar 

  89. Huang Y P, Goh T, Liew C L. Hunting suicide notes in Web 2.0 — preliminary findings. In: Proceedings of the 9th IEEE International Symposium on Multimedia Workshops. 2007, 517–521

  90. Tan L K, Na J C, Theng Y L, Chang K. Phrase-level sentiment polarity classification using rule-based typed dependencies. Journal of Computer Science and Technology, 2011, 27(3): 650–666

    Article  Google Scholar 

  91. 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

  92. Thelwall M, Buckley K, Paltoglou G. Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 2012, 63(1): 163–173

    Article  Google Scholar 

  93. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 2010, 61(12): 2544–2558

    Article  Google Scholar 

  94. Kaewpitakkun Y, Shirai K, Mohd M. Sentiment lexicon interpolation and polarity estimation of objective and out-of-vocabulary words to improve sentiment classification on microblogging. In: Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing, 2014, 204–213

  95. Cho H, Kim S, Lee J, Lee J S. Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews. Knowledge-Bused Systems, 2014, 71: 61–71

    Article  Google Scholar 

  96. Nielsen F. A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of Workshop on Making Sense of Microposts: Big Things Come in Small Packages. 2011, 93–98

  97. Cerini S, Compagnoni V, Demontis A, Formentelli M, Gandini G. Micro-WNOp: a gold standard for the evaluation of automatically compiled lexical resources for opinion mining. In: Proceedings of Language Resources and Linguistic Theory: Typology, Second Language Acquisition, English Linguistics. 2007, 200–210

  98. De Albornoz J C, Plaza L, GervAis P. SentiSense: an easily scalable concept-based affective lexicon for sentiment analysis. In: Proceedings of the 8th International Conference on Language Resources and Evaluation. 2012, 23–25

  99. Moreo A, Romero M, Castro J, Zurita J M. Lexicon-based comments-oriented news sentiment analyzersystem. Expert Systems with Applications, 2012, 39(10): 9166–9180

    Article  Google Scholar 

  100. Soni V, Patel M R. Unsupervised opinion mining from text reviews using SentiWordNet. International Journal of Computer Trends, 2014, 11(5): 234–238

    Google Scholar 

  101. Rout J K, Choo K K R, Dash A K, Bakshi S, Jena S K, Williams K L. A model for sentiment and emotion analysis of unstructured social media text. Electronic Commerce Research, 2018, 18(1): 181–199

    Article  Google Scholar 

  102. Attik M, Saad Missen M M, Coustaty M, Choi G S, Alotaibi F S, Akhtar N, Jhandir M Z, Prasath V B S, Salamat N, Husnain M. OpinionML—pinion markup language for sentiment representation. Symmetry, 2020, 12(2): 187–224

    Article  Google Scholar 

  103. Saif H, Fernandez M, He Y, Alani H. SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter. In: Proceedings of European Semantic Web Conference. 2014, 83–98

  104. Heerschop B, Hogenboom A, Frasincar F. Sentiment lexicon creation from lexical resources. In: Proceedings of International Conference on Business Information Systems. 2011, 185–196

  105. Heerschop B, Goossen F, Hogenboom A, Frasincar F, Kaymak U, de Jong F. Polarity analysis of texts using discourse structure. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. 2011, 1061–1070

  106. Zaki M J. SPADE: an efficient algorithm for mining frequent sequences. Machine Learning, 2001, 41(1): 31–60

    Article  MATH  Google Scholar 

  107. Whitehead M, Yaeger L. Building a general purpose cross-domain sentiment mining model. In: Proceedings of World Congress on Computer Science and Information Engineering. 2009, 472–476

  108. Zhang E, Zhang Y. UCSC on TREC 2006 blog opinion mining. In: Proceedings of Text Retrieval Conference. 2006, 1–3

  109. Ounis I, Macdonald C, Soboroff I. Overview of the TREC-2008 Blog Track. In: Proceedings of the 19th Text REtrieval Conference. 2010, 1–13

  110. Ngo J, Cheng L. Feature-based extraction using typed dependencies on political commentaries. In: Proceedings of PACLING 2013:Conference of the Pacific Association for Computational Linguistics. 2011, 93–95

  111. Singh V K, Piryani R, Uddin A, Waila P. Sentiment analysis of movie reviews: a new feature-based heuristic for aspect-level sentiment classification. In: Proceedings of International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing. 2013, 712–717

  112. Rana T A, Cheah Y N. Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 2016, 46(4): 459–483

    Article  Google Scholar 

  113. Penalver-Martinez I, Garcia-Sanchez F, Valencia-Garcia R, Rodriguez-Garcia M A, Moreno V, Fraga A, Sanchez-Cervantes J L. Feature-based opinion mining through ontologies. Expert Systems with Applications, 2014, 41(13): 5995–6008

    Article  Google Scholar 

  114. Liang P W, Dai B R. Opinion mining on social media data. In: Proceedings of IEEE International Conference on Mobile Data Management. 2013, 91–96

  115. O’Reilly T. What Is Web 2.0: design patterns and business models for the next generation of software. Communications & Strategies, 2007, 1(1): 17–35

    Google Scholar 

  116. Chalothorn T, Ellman J. Affect analysis of radical contents on web forums using SentiWordNet. International Journal of Innovation, Management and Technology, 2013, 4(1): 122–124

    Google Scholar 

  117. Chalothorn T, Ellman J. Using SentiWordNet and sentiment analysis for detecting radical content on web forums. In: Proceedings of the 6th Conference on Software, Knowledge, Information Management and Applications. 2012, 9–11

  118. Hamouda A, Rohaim M. Reviews classification using SentiWordNet lexicon. In: Proceedings of the World Congress on Computer Science and Information Technology. 2011, 104–105

  119. Ohana B, Tierney B. Sentiment classification of reviews using SentiWordNet. In: Proceedings of IT & T Conference. 2009, 19–26

  120. Kumar V, Minz S. Mood classification of lyrics using SentiWordNet. In: Proceedings of International Conference on Computer Communication and Informatics. 2013, 1–5

  121. Gatti L, Guerini M. Assessing sentiment strength in words prior polarities. In: Proceedings of the 24th International Conference on Computational Linguistics. 2012, 361–365

  122. Kiritchenko S, Zhu X, Mohammad S M. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 2014, 50: 723–762

    Article  Google Scholar 

  123. Margaret M, Bradley P J L. Affective norms for english words (ANEW). Instruction Manual and Affective Ratings, 1999, 30(1): 25–36

    Google Scholar 

  124. Rill S, Reinel D, Scheidt J, Zicari R V. Politwi: early detection of emerging political topics on Twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems, 2014, 69: 24–33

    Article  Google Scholar 

  125. Sohangir S, Petty N, Wang D. Financial sentiment lexicon analysis. In: Proceedings of the 12th International Conference on Semantic Computing. 2018, 286–289

  126. Quan C, Ren F. Unsupervised product feature extraction for feature-oriented opinion determination. Information Sciences, 2014, 272: 16–28

    Article  Google Scholar 

  127. Xu X, Cheng X, Tan S, Liu Y, Shen H. Aspect-level opinion mining of online customer reviews. China Communications, 2013, 10(3): 25–41

    Article  Google Scholar 

  128. Mukherjee S, Joshi S. Sentiment aggregation using ConceptNet ontology. In: Proceedings of the 6th International Joint Conference on Natural Language Processing. 2013, 570–578

  129. Thet T T, Na J C, Khoo C S, Shakthikumar S. Sentiment analysis of movie reviews on discussion boards using a linguistic approach. In: Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion. 2009, 81–84

  130. Loughran T, McDonald B. The use of word lists in textual analysis. Journal of Behavioral Finance, 2015, 16(1): 1–6

    Article  Google Scholar 

  131. Marrese-Taylor E, Velásquez J D, Bravo-Marquez F. A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Systems with Applications, 2014, 41(17): 7764–7775

    Article  Google Scholar 

  132. Gezici G, Yanikoğlu B, Tapucu D, Saygin Y. New features for sentiment analysis: do sentences matter? In: Proceedings of the 1st International Workshop on Sentiment Discovery from Affective Data. 2012, 5–15

  133. Lefter I, Burghouts G J, Rothkrantz L J. Recognizing stress using semantics and modulation of speech and gestures. IEEE Transactions on Affective Computing, 2015, 7(2): 162–175

    Article  Google Scholar 

  134. Nassirtoussi A K, Aghabozorgi S, Wah T Y, Ngo D C. Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Systems with Applications, 2015, 42(1): 306–324

    Article  Google Scholar 

  135. Qazi A, Tamjidyamcholo A, Raj R G, Hardaker G, Standing C. Assessing consumers’ satisfaction and expectations through online opinions: expectation and disconfirmation approach. Computers in Human Behavior, 2017, 75: 450–460

    Article  Google Scholar 

  136. Mukherjee S, Joshi S. Author-specific sentiment aggregation for polarity prediction of reviews. In: Proceedings of the 9th International Conference on Language Resources and Evaluation. 2014, 3092–3099

  137. Chaovalit P, Zhou L. Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences. 2005

  138. Cambria E. An introduction to concept-level sentiment analysis. In: Proceedings of Mexican International Conference on Artificial Intelligence. 2013, 478–483

  139. Hamon K W. Blogs, wikis, podcasts, and other powerful web tools for classrooms. Organization Management Journal, 2011, 8(2): 129–131

    Article  Google Scholar 

  140. Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004, 168–177

  141. Thelwall M, Buckley K, Paltoglou G. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 2011, 62(2): 406–418

    Article  Google Scholar 

  142. Khan F H, Qamar U, Bashir S. SentiMI: introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection. Applied Soft Computing, 2016, 39: 140–153

    Article  Google Scholar 

  143. Mohammad S, Shutova E, Turney P. Metaphor as a medium for emotion: an empirical study. In: Proceedings of the 5th Joint Conference on Lexical and Computational Semantics. 2016, 23–33

  144. Cambria E, Schuller B, Xia Y, Havasi C. New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 2013, 28(2): 15–21

    Article  Google Scholar 

  145. Joshi A, Bhattacharyya P, Carman M J. Automatic sarcasm detection: a survey. ACM Computing Surveys (CSUR), 2017, 50(5): 1–22

    Article  Google Scholar 

  146. Clavel C, Callejas Z. Sentiment analysis: from opinion mining to human-agent interaction. IEEE Transactions on Affective Computing, 2015, 7(1): 74–93

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Department of Computer Science & IT, The Islamia University of Bahawalpur, Pakistan in collaboration with Laboratoire Informatique, Image et Interaction (L3i), University of La Rochelle, France.

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Correspondence to Malik Muhammad Saad Missen.

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Mujtaba Husnain is working as an assistant professor in the Department of Computer Science & IT, The Islamia University of Bahawalpur (IUB), Pakistan. He is also a PhD student from Department of Computer Sciences, The Islamia University of Bahawalpur, Pakistan. He completed his MS in 2006 with specialization in Machine Learning from School of Sciences and Technology, University of Management and Technology (UMT), Pakistan. He is the recipient of a number of awards, scholarships and research grants i.e., Higher Education Commission (HEC) Indigenous Scholarship for Higher Studies in 2005, PERIDOT scholarship for collaborative research study in Laboratoire Informatique, Image et Interaction (L3i) in La Rochelle, France in 2017 and 2018. His research areas are Data Visualization, Information retrieval, Image Processing and Machine Learning. He has been a university faculty member teaching graduate, post-graduate students, and supervising the research activities at graduate and post-graduate level.

Malik Muhammad Saad Missen is an assistant professor in Department of Computer Science, The Islamia University of Bahawalpur, Pakistan. He completed his PhD (Information Retrieval) Universite de Paul Sabatier, Toulouse France (Lab: Institu de Recherche en Informatique de Toulouse). He published a number of research articles on opinion mining and information retrieval. He has also received a number of local and international grants and scholarship for higher studies and research like Higher Education Commission scholarship for PhD in France, PERIDOT scholarship for collaborative research in France in 2016 and 2017. His area of research is information retrieval/processing, Web usability engineering, software quality assurance.

Nadeem Akhtar is working as an assistant professor at the Department of Computer Science & IT, The Islamia University of Bahawalpur (IUB), Pakistan. He has a PhD from Laboratory IRISA of Computer Science, University of South Brittany (UBS), European University of Brittany (UEB), Bretagne, France with honor “Tres Honorable”. He completed his MS (Master-2) with specialization in Information System Architecture from Institut Universitaire Professionnalisé (IUP), University of South Brittany, Bretagne, France. He is the recipient of a number of awards, scholarships and research grants i.e., Study in France 2004 French Embassy scholarship for Master studies, Higher Education Commission (HEC) overseas scholarship 2006 for PhD studies in France, Teaching assistant for ENSIBS — UBS France, HEC Start-up research grant of 0.5 million in 2012, Student research project grant from ICT in 2014. His research areas are formal validation, formal modeling, safety-critical systems, formal verification, multi-agent systems, system-of-systems and software architecture. He has been a university faculty member teaching graduate, post-graduate students, and supervising PhD and MS (Computer Science) research.

Mickaël Coustaty is with Faculties of Science and Technology, University of La Rochelle, France. He is an active member of computer societies like IEEE, ACM, CRA, etc. His area of research is document analysis, digital image processing, machine learning.

Shahzad Mumtaz is an assistant professor in Department of Computer Science, The Islamia University of Bahawalpur, Pakistan. He is PhD from Aston University, United Kingdom. He is an active researcher in area of Machine Learning and Data Visualization. He is author of several research articles published in quality journals. He is recipient of scholarship for PhD from Higher Education Commission, Pakistan. His area of interest is machine learning/data science/data mining/statistical pattern analysis in general but with a particular interest in probabilistic approaches of high-dimensional data projection approaches and their use in answering questions related to biological problems related to protein analytics, patient specific analytics, etc.

V. B. Surya Prasath is a mathematician with expertise in the application areas of image processing, computer vision, machine learning and data science. He received his PhD in mathematics from the Indian Institute of Technology Madras, India in 2009. He has been a postdoctoral fellow at the Department of Mathematics, University of Coimbra, Portugal, for two years from 2010 to 2011. From 2012 to 2015 he was with the Computational Imaging and VisAnalysis (CIVA) Lab at the University of Missouri, USA as a postdoctoral fellow, and from 2016 to 2017 as an assistant research professor. He is currently an assistant professor in the Division of Biomedical Informatics at the Cincinnati Children’s Hospital Medical Center, and at the Departments of Biomedical Informatics, Electrical Engineering and Computer Science, University of Cincinnati from 2018. He had summer fellowships/visits at Kitware Inc. NY, USA, The Fields Institute, Canada, and IPAM, University of California Los Angeles (UCLA), USA. His main research interests include nonlinear PDEs, regularization methods, inverse and ill-posed problems, variational, PDE based image processing, and computer vision with applications in remote sensing, biomedical imaging domains. His current research focuses are in data science, and bioimage informatics with machine learning techniques.

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Husnain, M., Missen, M.M.S., Akhtar, N. et al. A systematic study on the role of SentiWordNet in opinion mining. Front. Comput. Sci. 15, 154614 (2021). https://doi.org/10.1007/s11704-019-9094-0

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