A systematic study on the role of SentiWordNet in opinion mining
Mujtaba HUSNAIN, Malik Muhammad Saad MISSEN, Nadeem AKHTAR, Mickaël COUSTATY, Shahzad MUMTAZ, V. B. Surya PRASATH
A systematic study on the role of SentiWordNet in opinion mining
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-ofart 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.
opinion mining / lexical based resources / SentiWordNet / opinion strength
[1] |
Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1–167
CrossRef
Google scholar
|
[2] |
Liu B, Zhang L. A survey of opinion mining and sentiment analysis. Mining Text Data. 3rd ed. Springer, 2012
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[4] |
Picard R W. Affective computing: from laughter to IEEE. IEEE Transactions on Affective Computing, 2010, 1(1): 11–17
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[10] |
Singhal A. Modern information retrieval: a brief overview. IEEE Data Engineering Bulletin, 2011, 24(4): 35–43
|
[11] |
Liu B. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. 2nd ed. Cambridge University Press, 2016
CrossRef
Google scholar
|
[12] |
Herrera F. Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence, 2008, 1(1): 27–46
CrossRef
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
CrossRef
Google scholar
|
[14] |
Cambria E, Hussain A. Sentic computing: techniques, tools, and applications. Springer Science & Business Media, 2012, 59(2): 557–577
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[25] |
Esuli A. Automatic generation of lexical resources for opinion mining model. Association for Computing Machinery, 2008, 42(2): 105–106
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
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
|
[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
CrossRef
Google scholar
|
[33] |
Li W, Xu H. Text-based emotion classification using emotion cause extraction. Expert Systems with Applications, 2014, 41(4): 1742–1749
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[37] |
Shelke N. Survey of techniques for opinion mining. International Journal of Computer Applications, 2012, 57(13): 30–35
|
[38] |
Toutanova K, Klein D, Manning C D, Singer Y. Feature-rich part-ofspeech 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
CrossRef
Google scholar
|
[39] |
Chris D P. Another stemmer. In: Proceedings of the ACM SIGIR Forum. 1990, 56–61
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[42] |
Feldman R, Sanger J. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. 3rd ed. Cambridge University Press. 2007
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
Google scholar
|
[46] |
Tsytsarau M, Palpanas T. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 2012, 24(3): 478–514
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
|
[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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[59] |
Wiebe J, Wilson T, Cardie C. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 2005, 39(2): 165–210
CrossRef
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
|
[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 theMeaning 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
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
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 7th International 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
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
Google scholar
|
[88] |
Desmet B, Hoste V. Emotion detection in suicide notes. Expert Systems with Applications, 2013, 40(16): 6351–6358
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
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
CrossRef
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
CrossRef
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 commentsoriented news sentiment analyzer system. Expert Systems with Applications, 2012, 39(10): 9166–9180
CrossRef
Google scholar
|
[100] |
Soni V, Patel MR. Unsupervised opinion mining from text reviews using SentiWordNet. International Journal of Computer Trends, 2014, 11(5): 234–238
CrossRef
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
CrossRef
Google scholar
|
[102] |
Attik M, Saad Missen MM, 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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[106] |
Zaki M J. SPADE: an efficient algorithm for mining frequent sequences. Machine Learning, 2001, 41(1): 31–60
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[112] |
Rana T A, Cheah Y N. Aspect extraction in sentiment analysis: comparative analysis and survey. Artificial Intelligence Review, 2016, 46(4): 459–483
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[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
|
[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
|
[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
CrossRef
Google scholar
|
[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
CrossRef
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
|
[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
CrossRef
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
CrossRef
Google scholar
|
[126] |
Quan C, Ren F. Unsupervised product feature extraction for featureoriented opinion determination. Information Sciences, 2014, 272: 16–28
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[130] |
Loughran T, McDonald B. The use of word lists in textual analysis. Journal of Behavioral Finance, 2015, 16(1): 1–6
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[139] |
Hamon K W. Blogs, wikis, podcasts, and other powerful web tools for classrooms. Organization Management Journal, 2011, 8(2): 129–131
CrossRef
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
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[145] |
Joshi A, Bhattacharyya P, Carman M J. Automatic sarcasm detection: a survey. ACM Computing Surveys (CSUR), 2017, 50(5): 1–22
CrossRef
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
CrossRef
Google scholar
|
/
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