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The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9042))

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Abstract

Hitherto, sentiment analysis has been mainly based on algorithms relying on the textual representation of online reviews and microblogging posts. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling, and counting their words. But when it comes to interpreting sentences and extracting opinionated information, their capabilities are known to be very limited. Current approaches to sentiment analysis are mainly based on supervised techniques relying on manually labeled samples, such as movie or product reviews, where the overall positive or negative attitude was explicitly indicated. However, opinions do not occur only at document-level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a review. In order to overcome this and many other issues related to sentiment analysis, we propose a novel framework, termed concept-level sentiment analysis (CLSA) model, which takes into account all the natural-language-processing tasks necessary for extracting opinionated information from text, namely: microtext analysis, semantic parsing, subjectivity detection, anaphora resolution, sarcasm detection, topic spotting, aspect extraction, and polarity detection.

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References

  1. Agirre, E., Ansa, O., Hovy, E., Martínez, D.: Enriching very large ontologies using the www. arXiv preprint cs/0010026 (2000)

    Google Scholar 

  2. Andrzejewski, D., Zhu, X., Craven, M.: Incorporating domain knowledge into topic modeling via dirichlet forest priors. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 25–32. ACM (2009)

    Google Scholar 

  3. Baldwin, B.: Cogniac: high precision coreference with limited knowledge and linguistic resources. In: Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts, pp. 38–45. Association for Computational Linguistics (1997)

    Google Scholar 

  4. Barbu, C., Mitkov, R.: Evaluation tool for rule-based anaphora resolution methods. In: Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, pp. 34–41. Association for Computational Linguistics (2001)

    Google Scholar 

  5. Bell, D., Koulouri, T., Lauria, S., Macredie, R., Sutton, J.: Microblogging as a mechanism for human-robot interaction. Knowledge-Based Systems 69, 64–77 (2014)

    Article  Google Scholar 

  6. Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reynar, J.: Building a sentiment summarizer for local service reviews. In: Proceedings of WWW 2008 Workshop on NLP in the Information Explosion Era (2008)

    Google Scholar 

  7. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  8. Bloom, P.: Glue for the mental world. Nature 421, 212–213 (2003)

    Article  Google Scholar 

  9. Bonzanini, M., Martinez-Alvarez, M., Roelleke, T.: Opinion summarisation through sentence extraction: An investigation with movie reviews. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR 2012, pp. 1121–1122 (2012)

    Google Scholar 

  10. Branavan, S.R.K., Chen, H., Eisenstein, J., Barzilay, R.: Learning document-level semantic properties from free-text annotations. Journal of Artificial Intelligence Research 34(2), 569 (2009)

    MATH  Google Scholar 

  11. Brennan, S.E., Friedman, M.W., Pollard, C.J.: A centering approach to pronouns. In: Proceedings of the 25th Annual Meeting on Association for Computational Linguistics, pp. 155–162. Association for Computational Linguistics (1987)

    Google Scholar 

  12. Cambria, E.: An introduction to concept-level sentiment analysis. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013, Part II. LNCS, vol. 8266, pp. 478–483. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  13. Cambria, E., Fu, J., Bisio, F., Poria, S.: AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysis. In: AAAI, Austin, pp. 508–514 (2015)

    Google Scholar 

  14. Cambria, E., Gastaldo, P., Bisio, F., Zunino, R.: An ELM-based model for affective analogical reasoning. Neurocomputing 149, 443–455 (2015)

    Article  Google Scholar 

  15. Cambria, E., Hussain, A.: Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Springer, Cham (2015)

    Google Scholar 

  16. Cambria, E., Hussain, A., Havasi, C., Eckl, C., Munro, J.: Towards crowd validation of theUK national health service. In: WebSci, Raleigh (2010)

    Google Scholar 

  17. Cambria, E., Schuller, B., Liu, B., Wang, H., Havasi, C.: Knowledge-based approaches to concept-level sentiment analysis. IEEE Intelligent Systems 28(2), 12–14 (2013)

    Article  Google Scholar 

  18. Cambria, E., Song, Y., Wang, H., Howard, N.: Semantic multi-dimensional scaling for open-domain sentiment analysis. IEEE Intelligent Systems 29(2), 44–51 (2014)

    Article  Google Scholar 

  19. Cao, C., Feng, Q., Gao, Y., Gu, F., Si, J., Sui, Y., Tian, W., Wang, H., Wang, L., Zeng, Q., et al.: Progress in the development of national knowledge infrastructure. Journal of Computer Science and Technology 17(5), 523–534 (2002)

    Article  MATH  Google Scholar 

  20. Cardie, C., Wagstaff, K., et al.: Noun phrase coreference as clustering. In: Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 82–89 (1999)

    Google Scholar 

  21. Chen, M., Jin, X., Shen, D.: Short text classification improved by learning multi-granularity topics. In: IJCAI, pp. 1776–1781. Citeseer (2011)

    Google Scholar 

  22. Chen, W.L., Zhu, J.B., Yao, T.S., Zhang, Y.X.: Automatic learning field words by bootstrapping. In: Proc. of the JSCL, vol. 72, Tsinghua University Press, Beijing (2003)

    Google Scholar 

  23. Chen, Z., Liu, B.: Mining topics in documents: standing on the shoulders of big data. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1116–1125. ACM (2014)

    Google Scholar 

  24. Chikersal, P., Poria, S., Cambria, E.: SeNTU: Sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval 2015) (2015)

    Google Scholar 

  25. Cohen, W.W., Hirsh, H.: Joins that generalize: Text classification using whirl. In: KDD, pp. 169–173 (1998)

    Google Scholar 

  26. Dagan, I., Itai, A.: Automatic processing of large corpora for the resolution of anaphora references. In: Proceedings of the 13th Conference on Computational Linguistics, vol. 3, pp. 330–332. Association for Computational Linguistics (1990)

    Google Scholar 

  27. Dann, S.: Twitter content classification. First Monday 15(12) (2010)

    Google Scholar 

  28. Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 241–249. Association for Computational Linguistics (2010)

    Google Scholar 

  29. Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcastic sentences in twitter and amazon. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 107–116. Association for Computational Linguistics (2010)

    Google Scholar 

  30. Denber, M.: Automatic resolution of anaphora in english. Eastman Kodak Co. (1998)

    Google Scholar 

  31. Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of First ACM International Conference on Web Search and Data Mining (WSDM 2008), pp. 231–240. Stanford University, Stanford(2008)

    Google Scholar 

  32. Du, B., Tian, H., Wang, L., Lu, R.: Design of domain-specific term extractor based on multi-strategy. Computer Engineering 31(14), 159–160 (2005)

    Google Scholar 

  33. Elliott, C.D.: The Affective Reasoner: A Process Model of Emotions in a Multi-Agent System. PhD thesis, Northwestern University, Evanston (1992)

    Google Scholar 

  34. Gangemi, A., Presutti, V., Reforgiato, D.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Computational Intelligence Magazine 9(1), 20–30 (2014)

    Article  Google Scholar 

  35. Gelfand, B., Wulfekuler, M., Punch, W.F.: Automated concept extraction from plain text. In: AAAI 1998 Workshop on Text Categorization, pp. 13–17 (1998)

    Google Scholar 

  36. Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: A deep learning approach. In: Proceedings of the Twenty-eight International Conference on Machine Learning, ICML (2011)

    Google Scholar 

  37. Goertzel, B., Silverman, K., Hartley, C., Bugaj, S., Ross, M.: The Baby Webmind project. In: AISB, Birmingham (2000)

    Google Scholar 

  38. González-Ibáñez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in twitter: a closer look. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol. 2, pp. 581–586. Association for Computational Linguistics (2011)

    Google Scholar 

  39. Harlambous, Y., Klyuev, V.: Thematically reinforced explicit semantic analysis. International Journal of Computational Linguistics and Applications 4(1), 79–94 (2013)

    Google Scholar 

  40. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 539–545. Association for Computational Linguistics (1992)

    Google Scholar 

  41. Hobbs, J.R.: Resolving pronoun references. Lingua 44(4), 311–338 (1978)

    Article  Google Scholar 

  42. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)

    Google Scholar 

  43. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 168–177 (2004)

    Google Scholar 

  44. Hu, Y., Boyd-Graber, J., Satinoff, B., Smith, A.: Interactive topic modeling. Machine Learning 95(3), 423–469 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  45. Jagarlamudi, J., Daumé III, H., Udupa, R.: Incorporating lexical priors into topic models. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 204–213. Association for Computational Linguistics (2012)

    Google Scholar 

  46. Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology 60(11), 2169–2188 (2009)

    Article  Google Scholar 

  47. 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, vol. 1, pp. 151–160. Association for Computational Linguistics (2011)

    Google Scholar 

  48. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, Springer, Heidelberg (1998)

    Google Scholar 

  49. Jung, S., Segev, A.: Analyzing future communities in growing citation networks. Knowledge-Based Systems 69, 34–44 (2014)

    Article  Google Scholar 

  50. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. CoRR, abs/1404.2188 (2014)

    Google Scholar 

  51. Kennedy, C., Boguraev, B.: Anaphora for everyone: pronominal anaphora resoluation without a parser. In: Proceedings of the 16th Conference on Computational Linguistics, vol. 1, pp. 113–118. Association for Computational Linguistics (1996)

    Google Scholar 

  52. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg! In: ICWSM, vol. 11, pp. 538–541 (2011)

    Google Scholar 

  53. Lappin, S., Leass, H.J.: An algorithm for pronominal anaphora resolution. Computational linguistics 20(4), 535–561 (1994)

    Google Scholar 

  54. Li, X., Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. Knowledge-Based Systems 69, 14–23 (2014)

    Article  Google Scholar 

  55. Liang, T., Wu, D.-S.: Automatic pronominal anaphora resolution in english texts. In: ROCLING (2003)

    Google Scholar 

  56. Liebrecht, C.C., Kunneman, F.A., van den Bosch, A.P.J.: The perfect solution for detecting sarcasm in tweets# not. In: ACL (2013)

    Google Scholar 

  57. Liu, F., Weng, F., Wang, B., Liu, Y.: Insertion, deletion, or substitution?: normalizing text messages without pre-categorization nor supervision. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol. 2, pp. 71–76. Association for Computational Linguistics (2011)

    Google Scholar 

  58. Liu, L., Cao, C., Wang, H.: Acquiring hyponymy relations from large chinese corpus. WSEAS Transactions on Business and Economics 2(4), 211 (2005)

    Google Scholar 

  59. Liu, L., Cao, C.-G., Wang, H.-T., Chen, W.: A method of hyponym acquisition based on “isa” pattern. Journal of Computer Science, 146–151 (2006)

    Google Scholar 

  60. Lu, Y., Zhai, C.: Opinion integration through semi-supervised topic modeling. In: Proceedings of the 17th International Conference on World Wide Web, pp. 121–130. ACM (2008)

    Google Scholar 

  61. Lu, Y., Zhai, C.X., Sundaresan, N.: Rated aspect summarization of short comments. In: Proceedings of the 18th International Conference on World Wide Web, pp. 131–140. ACM (2009)

    Google Scholar 

  62. Mcauliffe, J.D., Blei, D.M.: Supervised topic models. In: Advances in Neural Information Processing Systems, pp. 121–128 (2008)

    Google Scholar 

  63. Mitkov, R.: Robust pronoun resolution with limited knowledge. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, vol. 2, pp. 869–875. Association for Computational Linguistics (1998)

    Google Scholar 

  64. Mitkov, R., Evans, R., Orăsan, C.: A new, fully automatic version of mitkov’s knowledge-poor pronoun resolution method. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 168–186. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  65. Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the Second Joint Conference on Lexical and Computational Semantics (SEMSTAR 2013) (2013)

    Google Scholar 

  66. Montejo-Raez, A., Diaz-Galiano, M., Martinez-Santiago, F., Urena-Lopez, A.: Crowd explicit sentiment analysis. Knowledge-Based Systems 69, 134–139 (2014)

    Article  Google Scholar 

  67. Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 339–348. Association for Computational Linguistics (2012)

    Google Scholar 

  68. Murphyp, G.L.: The big book of concepts. MIT Press (2002)

    Google Scholar 

  69. Murray, G., Carenini, G.: Subjectivity detection in spoken and written conversations. Natural Language Engineering 17, 397–418 (2011)

    Article  Google Scholar 

  70. Nakata, K., Voss, A., Juhnke, M., Kreifelts, T.: Collaborative concept extraction from documents. In: Proceedings of the 2nd Int. Conf. on Practical Aspects of Knowledge management (PAKM 1998). Citeseer (1998)

    Google Scholar 

  71. Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using em. Machine Learning 39(2-3), 103–134 (2000)

    Article  MATH  Google Scholar 

  72. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. In: ICWSM, vol. 11, pp. 122–129 (2010)

    Google Scholar 

  73. Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)

    Book  Google Scholar 

  74. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC (2010)

    Google Scholar 

  75. Paltoglou, G., Thelwall, M.: Twitter, myspace, digg: Unsupervised sentiment analysis in social media. ACM Transactions on Intelligent Systems and Technology (TIST) 3(4), 66 (2012)

    Google Scholar 

  76. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004) (2004)

    Google Scholar 

  77. Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: ACL, pp. 115–124. Ann Arbor (2005)

    Google Scholar 

  78. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: EMNLP, Philadelphia, pp. 79–86 (2002)

    Google Scholar 

  79. Polanyi, L., Zaenen, A.: Contextual valence shifters. In: Computing Attitude and Affect in text: Theory and Applications, pp. 1–10. Springer (2006)

    Google Scholar 

  80. Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP 2005), pp. 3–28 (2005)

    Google Scholar 

  81. Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., Howard, N.: Dependency-based semantic parsing for concept-level text analysis. In: Gelbukh, A. (ed.) CICLing 2014, Part I. LNCS, vol. 8403, pp. 113–127. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  82. Poria, S., Cambria, E., Hussain, A., Huang, G.-B.: Towards an intelligent framework for multimodal affective data analysis. Neural Networks 63, 104–116 (2015)

    Article  Google Scholar 

  83. Poria, S., Cambria, E., Ku, L.-W., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: COLING, Dublin, pp. 28–37 (2014)

    Google Scholar 

  84. Poria, S., Cambria, E., Winterstein, G., Huang, G.-B.: Sentic patterns: Dependency-based rules for concept-level sentiment analysis. Knowledge-Based Systems 69, 45–63 (2014)

    Article  Google Scholar 

  85. Poria, S., Gelbukh, A., Cambria, E., Hussain, A., Huang, G.-B.: EmoSenticSpace: A novel framework for affective common-sense reasoning. Knowledge-Based Systems 69, 108–123 (2014)

    Article  Google Scholar 

  86. Rajagopal, D., Cambria, E., Olsher, D., Kwok, K.: A graph-based approach to commonsense concept extraction and semantic similarity detection. In: WWW, pp. 565–570. Rio De Janeiro (2013)

    Google Scholar 

  87. Ramirez, P.M., Mattmann, C.A.: Ace: improving search engines via automatic concept extraction. In: Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, IRI 2004, pp. 229–234. IEEE (2004)

    Google Scholar 

  88. Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: EACL, Athens, pp. 675–682 (2009)

    Google Scholar 

  89. Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005)

    Google Scholar 

  90. Rill, S., Reinel, D., Scheidt, J., Zicari, R.: Politwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems 69, 14–23 (2014)

    Article  Google Scholar 

  91. Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112 (2003)

    Google Scholar 

  92. Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C.: Red opal: product-feature scoring from reviews. In: Proceedings of the 8th ACM Conference on Electronic Commerce, pp. 182–191. ACM (2007)

    Google Scholar 

  93. Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank (2013)

    Google Scholar 

  94. Somasundaran, S., Wiebe, J., Ruppenhofer, J.: Discourse level opinion interpretation. In: COLING, Manchester, pp. 801–808 (2008)

    Google Scholar 

  95. Song, Y., Wang, H., Wang, Z., Li, H., Chen, W.: Short text conceptualization using a probabilistic knowledgebase. In: IJCAI, Barcelona (2011)

    Google Scholar 

  96. Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 841–842. ACM (2010)

    Google Scholar 

  97. Stevenson, R., Mikels, J., James, T.: Characterization of the affective norms for english words by discrete emotional categories. Behavior Research Methods 39, 1020–1024 (2007)

    Article  Google Scholar 

  98. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specificword embedding for twitter sentiment classification (2014)

    Google Scholar 

  99. Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120. ACM (2008)

    Google Scholar 

  100. Tsur, O., Davidov, D., Rappoport, A.: Icwsm-a great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In: ICWSM (2010)

    Google Scholar 

  101. Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: ACL, Philadelphia, pp. 417–424 (2002)

    Google Scholar 

  102. Turney, P., Littman, M.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21(4), 315–346 (2003)

    Article  Google Scholar 

  103. Velardi, P., Fabriani, P., Missikoff, M.: Using text processing techniques to automatically enrich a domain ontology. In: Proceedings of the International Conference on Formal Ontology in Information Systems, vol. 2001, pp. 270–284. ACM (2001)

    Google Scholar 

  104. Velikovich, L., Goldensohn, S., Hannan, K., McDonald, R.: The viability of web-derived polarity lexicons. In: NAACL, Los Angeles, pp. 777–785 (2010)

    Google Scholar 

  105. Wang, F., Wang, Z., Li, Z., Wen, J.-R.: Concept-based short text classification and ranking. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1069–1078. ACM (2014)

    Google Scholar 

  106. Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783–792. ACM (2010)

    Google Scholar 

  107. Wang, T., Cai, Y., Leung, H.-F., Lau, R.Y.K., Li, Q.: Huaqing Min. Product aspect extraction supervised with online domain knowledge. Knowledge-Based Systems 71, 86–100 (2014)

    Article  Google Scholar 

  108. Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing, pp. 486–497 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  110. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: HLT/EMNLP, Vancouver (2005)

    Google Scholar 

  111. Yang, C., Lin, K.H., Chen, H.-H.: Emotion classification using web blog corpora. In: IEEE/WIC/ACM International Conference on Web Intelligence, pp. 275–278. IEEE (2007)

    Google Scholar 

  112. Yuntao, Z., Ling, G., Yongcheng, W., Zhonghang, Y.: An effective concept extraction method for improving text classification performance. Geo-Spatial Information Science 6(4), 66–72 (2003)

    Article  Google Scholar 

  113. Zhao, W.X., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a maxent-lda hybrid. In: EMNLP, pp. 56–65. Association for Computational Linguistics (2010)

    Google Scholar 

  114. Zheng, J.H., Lu, J.L.: Study of an improved keywords distillation method. Computer Engineering 31(18), 194–196 (2005)

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria, Soujanya Poria, Rajiv Bajpai & Iti Chaturvedi

  2. DITEN, University of Genoa, Genoa, Italy

    Federica Bisio

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  1. Erik Cambria
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  2. Soujanya Poria
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  3. Federica Bisio
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  4. Rajiv Bajpai
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  5. Iti Chaturvedi
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Correspondence to Erik Cambria .

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  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico DF, Mexico

    Alexander Gelbukh

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Cambria, E., Poria, S., Bisio, F., Bajpai, R., Chaturvedi, I. (2015). The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-18117-2_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18116-5

  • Online ISBN: 978-3-319-18117-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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