Review of Sentiment Analysis and Opinion Mining Algorithms

Abstract

Sentiment Analysis or Opinion Mining is an important field in text mining.  Nowadays the products which are produced by companies or persons are reached to consumers mercurially and reviews about these products issued on web pages. As understood easily these reviews are very significant for producers. In addition to that, Sentiment Analysis can be used from financial field to medicine field. Sentiment Analysis investigates a text that has a positive, negative or a neutral meaning. In general, we can imagine Sentiment Analysis as the computational treatment of opinions, sentiments, and subjectivity of text.

In this study, a research about Sentiment Analysis has been performed and Sentiment Analysis classification techniques have been explained with its all parts.  Many articles related with Sentiment Analysis have been studied and briefly explained. Then, one application about Sentiment Analysis has been shown for understanding more about Sentiment Analysis. Consequently, a general assessment of this issue has been done and the study has been finished with the result section.

Keywords

• Opinion mining,sentiment analysis,sentiment analysis algorithms,sentiment analysis applications

Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi

Abstract

Duygu Analizi veya Fikir Madenciliği, metin madenciliğinin önemli bir alanı ve son yılların önemli araştırma konularından biridir. Günümüzde şirketlerin veya kişilerin ürettikleri ürünler çok hızlı bir şekilde tüketiciye ulaşmakta ve bu ürünlerle ilgili yapılan yorumlarda gelişen teknoloji ile beraber internet dünyasına yansımaktadır. Bu yorumların ne anlama geldiği üreticiler için çok önemlidir. Bunun dışında Duygu Analizi veya Fikir Madenciliği finanstan tutun da tıp alanına kadar birçok alanda kullanılabilir. Duygu Analizi; bir metni ele alarak bu metnin olumlu, olumsuz veya tarafsız bir içeriğe sahip olup olmadığını inceler. Genel olarak fikirlerin, duyguların ve metinlerin nesnelliğinin hesaplanma işlemi de denilebilir.

Bu çalışma da, Duygu Analizi hakkında araştırma yapılmış olup Duygu Analizi sınıflandırma teknikleri incelenerek tüm alt bileşenleri ile beraber anlatılmıştır. Duygu Analizi ile ilgili birçok güncel makale incelenmiş ve kısaca anlatılmıştır. En son olarak genel bir değerlendirme ve sonuç yazılarak çalışma bitirilmiştir.

Keywords

• Duygu analizi,duygu analizi algoritmalari,duygu analizi uygulamalari,fikir madenciliği

References

1. Agarwal, A., Mittala, N., 2014. Semantic Feature Clustering for Sentiment Analysis of English Reviews, IETE Journal of Research. 60:6, 414-422.
2. Aggarwal, C. C., Zhai, C., 2012. A survey of text classification algorithms. In Mining text data, Springer US. pp. 163-222.
3. Aizerman, M., Braverman, E., Rozonoer, L., 1964. Theoretical foundations of the potential function method in pattern recognition learning. Autom Rem Cont. 821–37.
4. Al-Rowaily, K., Abulaish, M., Haldar, N.A.H., Al-Rubaian, M., 2015. BiSAL- A bilingual sentiment analysis lexicon to analyze Dark Web forums for cyber security. Digital Investigation 14 53-62.
5. Alvaro, O., José, M.M, Rosa, M.C., 2013, Sentiment analysis in Facebook and its application to e-learning, Computers in Human Behavior. 31;527-541.
6. Appel, O., Chiclana, F., Carter, J., Fujita, H., 2016. A hybrid approach to the sentiment analysis problem at the sentence level. Knowledge-Based Systems, 108;110–124.
7. Asher, N., Benamara, F., Mathieu, Y., 2008. Distilling Opinion in Discourse: A Preliminary Study. In COLING (Posters) (pp. 7-10).
8. Ayoub, B., Mohamad, S., Franciska, de J., 2013. Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews, Knowledge-Based Systems. 52;201-213.

9. Bai., X, 2011. Predicting consumer sentiments from online text. Decis Support Syst. 50:732–42.
10. Balahur, A., Hermida J.M., Montoyo, A., 2012, Detecting implicit expressions of emotion in text: a comparative analysis. Decis Support Syst, 53:742–53.
11. Balahur, A., Turchi, M., 2014. Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis, Computer Speech and Language. 28, 56-75.
12. Boldrini, E., Balahur, A., Martínez-Barco, P., Montoyo, A., 2012. Using EmotiBlog to annotate and analyse subjectivity in the new textual genres. Data Min Knowl Discov. 25: 603–34.
13. Bolshakov, I.A, Gelbukh, A., 2004, Synonymous paraphrasing using wordnet and internet. In: International Conference on Application of Natural Language to Information Systems. Springer Berlin Heidelberg, p. 312-323.
14. Bravo-Marquez, F., Mendoza, M., Poblete, B., 2014. Meta-level sentiment models for big social data analysis, Knowledge-Based Systems. 69, 86-99.
15. Cambria, E., Benson, T., Eckl, C., Hussain, A., 2012. Sentic PROMs: application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Syst Appl. 39:10533–43.
16. Cambria, E., Havasi C., Hussain, A., 2012. SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis." FLAIRS conference. p.202-207.
17. Cambria, E., Hussain, A., Havasi, C., Eckl, C., Munro, J., 2010. Towards crowd validation of the UK national health service. WebSci10, 1-5.
18. Camp, M., Bosch, A., 2012. The socialist network. Decis Support Syst. 53:761–9.
19. 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. 50:511–21.
20. Chakrabarti, S., Roy, S., Soundalgekar, M.V., 2003. Fast and accurate text classification via multiple linear discriminant projections. VLDB J.2:172–85.
21. Chan, S.W.K., Chong, M.C.V., 2017. Sentiment analysis in financial texts. Decision Support Systems. 94;53–64.
22. Chen, T., Xu, R., He, Y., Wang, X., 2017. Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems With Applications, 72; 221–230.
23. Chenlo, J., Hogenboom, A., Losada, D., 2013. Sentiment-based ranking of blog posts using rhetorical structure theory. In: International Conference on Application of Natural Language to Information Systems. Springer Berlin Heidelberg, p. 13-24.
24. Chien, C, C., You-De, T., 2011. Quality evaluation of product reviews using an information quality framework. Decis Support Syst. 50:755–68.
25. Cortes, C., Vapnik, V., 1995, Support-vector networks. Machine learning, 20(3), 273-297. Cover TM, Thomas JA. 1991. Elements of information theory. New York: John Wiley and Sons.
26. Deerwester, S., Dumais, S., Landauer, T., Furnas, G., Harshman, R., 1990. Indexing by latent semantic analysis. JASIS. 41:391–407.
27. Deng, S., Sinha, A.P., Zhao, H., 2017. Adapting sentiment lexicons to domain-specific social media texts, Decision Support Systems 94;65–76.
28. Di Caro, L., Grella, M., 2013. Sentiment analysis via dependency parsing. Computer Standards & Interfaces, 2013, 35.5: 442-453.
29. Diana, M., Adam, F., 2011. Automatic detection of political opinions in tweets. In: Proceedings of the 8th international conference on the semantic web, ESWC’11, p. 88–99.
30. Duric, A., Song, F., 2012. Feature selection for sentiment analysis based on content and syntax models. Decis Support Syst. 53:704–11.
31. Efstratios, K., Christos, B., Theologos, D., Nick, B., 2013, Ontology-based sentiment analysis of twitter posts, Expert Systems with Applications. 40;4065-4047.
32. Emma, H., Xiaohui, L., Yonnim, Lee., Yong, Shi., 2013. The Role of Text Pre-processing in Sentiment Analysis, Procedia Computer Science. 17; 26-32.
33. Fahrni, A., Klenner, M., 2008. Old wine or warm beer: target-specific sentiment analysis of adjectives. In: Proceedings of the symposium on affective language in human and machine, AISB. p. 60–3.
34. Fan, T. K., Chang, C. H., 2011. Blogger-centric contextual advertising. Expert Syst Appl. 38:1777–88.
35. Fattah, M.A., 2015. New term weighting schemes with combination of multiple classifiers for sentiment analysis. Neurocomputing, 167: 434-442.
36. Feldman, R., 2013. Techniques and applications for sentiment analysis. Commun ACM. 56:82–9.
37. Fermın, LC., Troyano Jose, A., Enrıquez F., Javier Ortega F, Vallejo, C.G., 2013. Long autonomy or long delay?’ The importance of domain in opinion mining. Expert Syst Appl. 40:3174–84.
38. Ganchev, K., Graca, J., Blitzer, J., Taskar, B., 2008. Multi-view learning over structured and non-identical outputs. In: Proceedings of the 24th conference on Uncertainty in Artificial Intelligence (UAI’08). p. 204–11.
39. Giatsoglou, M., Vozalis, M.G., Diamantaras, K., Vakali, A., Sarigiannidis, G., Chatzisavvas, K.C., 2017. Sentiment analysis leveraging emotions and word embeddings. Expert Systems With Applications, 69 (2017) 214–224.
40. Griffiths, T.L., Steyvers, M., Blei, D.M., Tenenbaum, J.B., 2005. Integrating topics and syntax. Adv Neural Inform Process Syst. 537–44.
41. Guo, K., Sun, Y., Qian, X., 2017. Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market. Physica A, 469;390–396.
42. Gupta, S. K., Phung, D., Adams, B., Venkatesh, S., 2012 Regularized nonnegative shared subspace learning. Data Min Knowl Discov. 26:57–97.
43. Habernal, I., Ptacek, T., Steinberger, J., 2014. Supervised sentiment analysis in Czech social media, Information Processing and Management. 50, 693-707.
44. Haeng-Jin, J., Jaemoon, S., Yonnim, L., Ohbyung, K., 2013. Ontology Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media, Expert Systems with Applications. 40;7492-7503.
45. Hagenau, M., Liebmann, M., Neumann, D., 2013. Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems, 55(3), 685-697.
46. Hatzivassiloglou, V., McKeown, K., 1997. Predicting the semantic orientation of adjectives. In: Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. Association for Computational Linguistics, p. 174-181.
47. He, Y., Zhou, D., 2011. Self-training from labeled features for sentiment analysis. Inf Process Manage. 47:606–16.
48. Heerschop, B., Goossen, F., Hogenboom, A., Frasincar, F., Kaymak, U., de Jong, F., 2011 Polarity analysis of texts using discourse structure. In: Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, p. 1061-1070.
49. Hogenboom, A., Heerschop, B., Frasincar, F., Kaymak, U., & de Jong, F., 2014. Multi-lingual support for lexicon based sentiment analysis guided by semantics, Decision Support Systems. 62;43-53.
50. Hu, M., Liu B., 2004. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, p. 168-177.
51. Hu, N., Bose, I., Koh, N. S., Liu, L., 2012. Manipulation of online reviews: an analysis of ratings, readability, and sentiments’. Decis Support Syst. 52:674–84.
52. Hu, Y., Wenjie, L., 2011. Document duygu classification by exploring description model of topical terms. Comput Speech Lang. 25:386–403.
53. Hu, Y., Wenjie, L., 2011. Document sentiment classification by exploring inopinion mining and sentiment analysis. IEEE Intell Sys. 28:15–21.
54. Hur, M., Kang, P., Cho, S., 2016. Box-office forecasting based on sentiments of movies reviews and Independent subspace method. Information Sciences 372 ;608–624.
55. Isa, N., Puteh, M., Kamarudin, R. M. H. R., 2013. Sentiment Classification of Malay Newspaper Using Immune Network (SCIN), Proceedings of the World Congress on Engineering 2013 Vol III, WCE 2013, July 3 – 5. London, U.K.
56. Jebaseeli, A.N., Kirubakara, E., 2013. Genetic Optimized Neural Network Algorithm to Improve Classification Accuracy for Opinion Mining of M-Learning Reviews, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS). 2, 345-349.
57. Jiao, J., Zhou, Y., 2011. Sentiment Polarity Analysis based multi-dictionary. Physics Procedia, 22:590-596. Joachims, T., 2002. Learning to classify text using support vector machines: methods, theory and algorithms. MA, USA: Norwell.
58. Joachims, T., 1996, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Carnegie-mellon univ pittsburgh pa dept of computer science.
59. Jolliffee, I.T., 2002. Principal component analysis. Springer.
60. Jurado, F., Rodriguez, P., 2015. Sentiment Analysis in monitoring software development processes: An exploratory case study on Git Hub’s Project issues, The Journal of Systems and Software. 104, 82-89.
61. Kang, H., Yoo, S. J., Han, D., Dongil, H., 2012. Senti-lexicon and improved Naıve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst Appl. 39:6000–10.
62. Katz, G., Ofek, N., Shapira, B., 2015. ConSent: Context-based sentiment analysis, Knowledge-Based Systems. 84, 162-178
63. Kaufmann, J.M., 2012. JMaxAlign: A Maximum Entropy Parallel Sentence Alignment Tool. In: Proceedings of COLING’12: Demonstration Papers, Mumbai. p. 277–88.
64. Keshtkar, F., Inkpen, D., 2013. A bootstrapping method for extracting paraphrases of emotion expressions from texts. Computational Intelligence, 29(3), 417-435.
65. Khan, F.H., Qamar, U., Bashir, S., 2016. SWIMS: Semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis. Knowledge-Based Systems 100;97–111.
66. Khan, F.H., Qamar, U., Bashir, S., 2016. eSAP: A decision support framework for enhanced sentiment analysis and polarity classification. Information Sciences 367–368;862–873.
67. Kim, S., Hovy, E., 2004. Determining the sentiment of opinions. In: Proceedings of the 20th international conference on Computational Linguistics. Association for Computational Linguistics. p. 1367
68. Ko, Y, Seo, J., 2000. Automatic text categorization by unsupervised learning. In: Proceedings of the 18th conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 453-459.
69. Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N., 2013. Ontology-based sentiment analysis of twitter posts. Expert Syst Appl. 40(10), 4065-4074.
70. Kranjc, J., Smailovic, J., Podpecan, V., Grcar, M., Znidaršic, M., Lavrac, N., 2014. Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform, Information Processing and Management. 51,187-203.
71. Lafferty, J., McCallum, A., Pereira, F., 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, ICML, p. 282-289.
72. Lambov, D., Pais, S., Dias, G., 2011 Merged agreement algorithms for domain independent sentiment analysis. Procedia-Social and Behavioral Sciences, 27: 248-257.
73. Lane, P.C., Clarke, D., Hender, P., 2012. On developing robust models for favourability analysis: Model choice, feature sets and imbalanced data. Decision Support Systems, 53(4), 712-718.
74. Lau, R. Y., Li, C., Liao, S. S., 2014. Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis, Decision Support Systems, 2014; 65,80-94.
75. Lewis, D.D., Ringuette, M., 1994. A comparison of two learning algorithms for text categorization. In: Third annual symposium on document analysis and information retrieval. p. 81-93.
76. Li, S., Tsai, F., 2011. Noise control in document classification based on fuzzy formal concept analysis. In: Fuzzy Systems (FUZZ), 2011 IEEE International Conference on. IEEE, p. 2583-2588.
77. Li, S.T., Tsai F.C., 2013. A fuzzy conceptualization model for text mining with application in opinion polarity classification. Knowl-Based Syst. 39: 23–33.
78. Li, Y., Jain, A., 1998. Classification of text documents. Comput J. 41: 537–46.
79. Li, Y.M, Li T.Y., 2013 Deriving market intelligence from microblogs. Decision Support Systems, 55(1), 206-217.
80. Liu, B., Hsu, W., Ma, Y., 1998. Integrating classification and association rule mining. In: Proceedings of the 4th.
81. Liu, B., 2012. Sentiment analysis and opinion mining. Morgan & Claypool Publishers.
82. Liu, Y., Bi, J.W, Fan, Z.P., 2017. Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion 36;149–161.
83. Lu, CY., Lin, S.H., Liu, J.C., Cruz-Lara, S., Hong, J.S., 2010. Hong Jen-Shin. Automatic event-level textual emotion sensing using mutual action histogram between entities. Expert Syst Appl. 37:1643–53.
84. Lund, K., Burgess, C., 1996. Producing high-dimensional semantic spaces from lexical co-occurrence. Behav Res Methods.28:203–8.
85. Maks, I., Vossen, P., 2012. A lexicon model for deep sentiment analysis and opinion mining applications. Decis Support Syst. 53:680–8.
86. Mann, W., Thompson, S., 1998. Rhetorical structure theory: toward a functional theory of text organization. Text. 8, 243–28.
87. Martın-Valdivia, M.T., Martınez-Camara, E., Perea-Ortega, J.M., Urena-Lopez, A.L., 2013, Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches. Expert Syst Appl. 40(10), 3934-3942.
88. Medhat, W., Hassan, A., Korashy, H., 2008. Combined algorithm for data mining using association rules. Ain Shams J Electric Eng. 1(1).
89. Medhat, W., Hassan, A., Korashy, H., 2014. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5:1093-1113.
90. Meire, M., Ballings, M., Van den Poel, D., 2016. The added value of auxiliary data in sentiment analysis of Facebook posts. Decision Support Systems. 89;98–112.
91. Meral, M., Diri, B., 2014. Sentiment analysis on Twitter. In: Signal Processing and Communications Applications Conference (SIU), 2014 22nd. IEEE, p. 690-693.
92. Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K., 1990. WordNet: an on-line lexical database. Oxford Univ. Press.
93. Min, H. J., Park, J. C., 2012. Identifying helpful reviews based on customer’s mentions about experiences. Expert Syst Appl. 39:11830–8.
94. Mohammad, S., Dunne, C., Dorr, B., 2009. Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2-Volume 2. Association for Computational Linguistics, p. 599-608.
95. Mohammad, S.M., 2012. From once upon a time to happily ever after: tracking emotions in mail and books. Decis Support Syst. 53:730–41.
96. Montoyo, A., MartíNez-Barco, P., Balahur, A., 2012. Subjectivity and sentiment analysis: an overview of the current state of the area and envisaged developments. Decis Support Syst. 53:675–9.
97. Moraes, R., Valiati J.F., Gaviao Neto, W.P., 2013. Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl. 40: 621–33.
98. Moreo, A., Romero, M., Castro, J.L., Zurita, J.M., 2012. Lexicon-based comments-oriented news sentiment analyzer system. Expert Syst Appl. 39:9166–80.
99. Mudinas, A., Zhang, D., Levene, M., 2012. Combining lexicon and learning based approaches for concept-level sentiment analysis. In: Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining. ACM, p. 5.
100. Neppalli, V.K., Caragea, C., Squicciarini, A., Tapia, A., Stehle, S., 2017. Sentiment analysis during Hurricane Sandy in emergency response. International Journal of Disaster Risk Reduction 21;213–222.
101. Neviarouskaya, A., Prendinger, H., Ishizuka, M., 2010. Recognition of Affect, Judgment, and Appreciation in Text. In: Proceedings of the 23rd international conference on computational linguistics, Beijing. p. 806–14.
102. Neviarouskaya, A., Prendinger, H., Ishizuka, M., 2007. Recognition of affect conveyed by text messaging in online communication. Online Communities and Social Computing, 141-150.
103. Neviarouskaya, A., Prendinger, H., Ishizuka, M., 2009, Compositionality Principle in Recognition of Fine-Grained Emotions from Text. In: ICWSM.
104. Neviarouskaya, A., Tsetserukou, D., Prendinger, H., Kawakami, N., Tachi, S., Ishizuka, M., 2009. Emerging system for affectively charged interpersonal communication. In: ICCAS-SICE, 2009. IEEE, p. 3376-3381.
105. Ng, H.T, Goh, Wei, Low, Kok., 1997. Feature selection, perceptron learning, and a usability case study for text categorization. In: ACM SIGIR Forum. ACM, p. 67-73.
106. Nizam, H., Akın, S.S., 2014. Sosyal Medyada Makine Öğrenmesi ile Duygu Analizinde Dengeli ve Dengesiz Veri Setlerinin Performanslarının Karşılaştırılması, XIX. Türkiye'de İnternet Konferansı.
107. Ortigosa-Hernández, J., Rodríguez, J. D., Alzate, L., Lucania, M., Inza, I., Lozano, J.A., 2012. Approaching sentiment analysis by using semi-supervised learning of multi-dimensional classifiers. Neurocomputing. 92:98–115.
108. Pai, M.Y., Chu H.C., Wang, S.C, Chen, Y.M., 2013. Electronic word of mouth analysis for service experience. Expert Syst Appl. 40:1993–2006.
109. Pang, B., Lee, L., 2008. Opinion mining and sentiment analysis. FoundTrends Inform Retriev. 2:1–135.
110. Piryani, R., Madhavi, D., Singh, V.K., 2017. Analytical mapping of opinion mining and sentiment analysis research during 2000-2015. Information Processing and Management, 53;122–150.
111. Plutchik, R., 1980. A general psychoevolutionary theory of emotion. Emotion: Theory Res Exp. 1:3–33.
112. Poria, S., Cambria, E., Howard, N., Huang, G.B., Hussain, A., 2016. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing.174. 50–59.
113. Priss, U., 2006. Formal concept analysis in information science. Arist, 40.1: 521-543.
114. Pröllochs, N., Feuerriegel, S., Neumann, D., 2016. Negation scope detection in sentiment analysis: Decision support for news-driven trading. Decision Support Systems, 88;67–75.
115. Ptaszynski, M., Dokoshi H., Oyama, S., Rzepka, R., Kurihara, M., Araki, K., Momouchi, Y., 2013. Affect analysis in context of characters in narratives. Expert Syst Appl. 40:168–76.
116. Qiu, G., He, X., Zhang, F., Shi, Y., Bu, J., & Chen, C., 2010. DASA: dissatisfaction-oriented advertising based on sentiment analysis. Expert Syst Appl. 37:6182–91.
117. Quinlan, J.R., 1986. Induction of decision trees. Machine Learn. 1:81–106.
118. Ratnaparkhi, A., 1996. A maximum entropy model for part-of-speech tagging. In: Proceedings of the conference on empirical methods in natural language processing. p. 133-142.
119. Read, J., Carroll, J., 2009 Weakly supervised techniques for domain-independent sentiment classification. In: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion. ACM, p. 45-52.
120. Reyes, A., Rosso, P., 2012. Making objective decisions from subjective data: detecting irony in customer reviews. Decis Support Syst. 53:754–60.
121. Robaldo, L., and Di Caro, L., 2013. Opinionmining-ml. Computer Standards & Interfaces, 35.5: 454-469.
122. Rui, H., Liu, Y., Whinston, A., 2013. Whose and what chatter matters? The effect of tweets on movie sales. Decis Support Syst. 55(4), 863-870.
123. Ruiz, M., Srinivasan, P., 1999. Hierarchical neural networks for text categorization (poster abstract). In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, p. 281-282.
124. Schumaker, R.P., Jarmoszko, A.T., Labedz, C.S., 2016. Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decision Support Systems 88;76–84.
125. Shi, W., Wang, H., He, S., 2013. Sentiment analysis of Chinese microblogging based on sentiment ontology: a case study of ‘7.23 Wenzhou Train Collision’, Computer Science. 25: 4, 161-178.
126. Smailovic, J., Grcar, M., Lavrac, N., Znidaršic, M., 2014. Stream-based active learning for sentiment analysis in the financial domain, Information Sciences. 285, 181-203.
127. Somasundaran, S., Namata, G., Wiebe, J., Getoor, L., 2009. Supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, p. 170-179.
128. Somasundaran, S., Wiebe, J., Ruppenhofer, J., 2008. Discourse level opinion interpretation. In: Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1. Association for Computational Linguistics, p.801-808.
129. Somasundaran, S., Wiebei, J., 2009 Recognizing stances in online debates. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1. Association for Computational Linguistics, p. 226-234.
130. Steinberger, J., Ebrahim, M., Ehrmann, M., Hurriyetoglu, A., Kabadjov, M., Lenkova, P., Zavarella, V., 2012. Creating sentiment dictionaries via triangulation. Decis Support Syst. 53: 689–94.
131. Sun, X., Li, C., Ren, F., 2016. Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features. Neurocomputing. 210;227–236.
132. Tan, S., Wang, Y., 2011. Weighted SCL model for adaptation of sentiment classification. Expert Syst Appl. 38: 10524–31.
133. Tan, S., Wu, Q., 2011. A random walk algorithm for automatic construction of oriented sentiment lexicon. Expert Syst Appl. 12094–100.
134. Tsytsarau, M., Palpanas, T., 2012. Survey on mining subjectivedata on the web. Data Min Knowledge Discovery. 24: 478–514.
135. Turney, P., 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, p. 417-424.
136. Van de Kauter, M., Breesch, D., Hoste, V., 2015. Fine-grained analysis of explicit and implicit sentiment in financial news articles, Expert Systems with Applications, 42, 4999-5010.
137. Vapnik, V., 1995, The nature of statistical learning theory, New York.
138. Walker, M.A, Anand, P., Abbott, R., Fox Tree, J.E., Craig, M., King, J., 2012. That is your evidence?: Classifying stance in online political debate. Decis Support Syst. 53: 719–29.
139. Wan, X., 2009. Co-training for cross-lingual sentiment classification. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-volume 1. Association for Computational Linguistics, p. 235-243.
140. Wang, Q., Jin, Y., Yang, T., Cheng, S., 2017. An emotion-based independent cascade model for sentiment spreading. Knowledge-Based Systems, 116;86–93.
141. Whitelaw, C., Garg, N., Argamon, S., 2005. Using appraisal groups for sentiment analysis. In: Proceedings of the ACM SIGIR Conference on Information and Knowledge Management (CIKM). p. 625–31.
142. Wille, R., 1982. Restructuring lattice theory: an approach based on hierarchies of concepts. In: I. Rival, Reidel, Dordrecht-Boston. p. 445–70.
143. Williams, L., Bannister, C., Arribas-Ayllon, M., Preece, A., Spasic, I., 2015. The role of idioms in sentiment analysis. Expert Systems with Applications 42;7375–7385.
144. Wilson, T., Wiebe, J., Hoffman, P., 2005 Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, p. 347-354
145. Wu, F., Huang, Y., Yuan, Z., 2017. Domain-specific sentiment classification via fusing sentiment knowledge from multiple sources. Information Fusion 35;26–37.
146. Wu, Q., Songbo, T., 2011. A two-stage framework for crossalan sentiment classification. Expert Syst Appl. 38:14269–75.
147. Xianghua, F., Guo., L, Yanyan, G., Zhiqiang, W., 2013. Multiaspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowl-Based Syst. 37:186–95.
148. Xu, K., Liao, S. S., Li, J., Song, Y., 2011.Mining comparative opinions from customer reviews for competitive intelligence. Decis Support Syst. 50: 743–54.
149. Xu, T., Peng, Q., Cheng, Y., 2012. Identifying the semanticorientation of terms using S-HAL for sentiment analysis. Knowl-Based Syst. 35:279–89.
150. Yan-Yan, Z., Bing, Q., Ting, L., 2010. Integrating intra-and inter-document evidences for improving sentence sentiment classification. Acta Automatica Sinica, 36.10: 1417-1425.
151. Yelena, M., Padmini, S., 2011. Exploring Feature Definition and Selection for Sentiment Classifiers. In: ICWSM.
152. Yu, L.C., Wu, J.L., Chann, P.C., Chu, H.S., 2013. Using a contextual entropy model to expand emotionwords and their intensity for the sentiment classification of stockmarket news. Knowl-Based Syst. 41:89–97.
153. Yu, Y., Wang. X., 2015. World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans’ tweets, Computers in Human Behavior. 48, 392-400.
154. Zhang, T., Johnson, D., 2003. A robust risk minimization based named entity recognition system. In: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4. Association for Computational Linguistics, p. 204-207.
155. Zhang, W., Xu, H., Wan, W., 2012. Weakness finder: find product weakness from Chinese reviews by using aspects based sentiment analysis. Expert Syst Appl. 39:10283–91.
156. Zhao, Y., Niu, K., He, Z., Lin, J., Wang, X., 2013. Text sentiment analysis algorithm optimization and platform development in social network. In: Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on. IEEE, p. 410-413.
157. Zhou, L., Li, B., Gao, W., Wei, Z., Wong, K., 2011. Unsupervised discovery of discourse relations for eliminating intra-sentence polarity ambiguities. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, p. 162-171.
158. Zirn, C., Niepert, M., Stuckenschmidt, H., Strube, M., 2011. Fine-Grained Sentiment Analysis with Structural Features. In: IJCNLP. p. 336-344.