Using data-driven feature enrichment of text representation and ensemble technique for sentence-level polarity classification

Journal of Information Science

Volumn 41, Issue 4, 2015, Pages 531-549

  Zhang, Pu ^a,b   He, Zhongshi ^a  

^a CHONGQING UNIVERSITY   (China)

^b CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

sentence polarity classification; Sentiment analysis; topic model; word embeddings

Indexed keywords

EMBEDDINGS; SENTIMENT ANALYSIS;

DATA REPRESENTATIONS; DATA SPARSENESS PROBLEM; ENSEMBLE APPROACHES; ENSEMBLE TECHNIQUES; OPINION RETRIEVALS; POLARITY CLASSIFICATION; TEXT REPRESENTATION; TOPIC MODELING;

CLASSIFICATION (OF INFORMATION);

EID: 84936995312     PISSN: 01655515     EISSN: 17416485     Source Type: Journal    
DOI: 10.1177/0165551515585264     Document Type: Article

References (37)

1. Liu B,. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012; 5 (1): 1.
2. Esuli A, Sebastiani F,. Determining the semantic orientation of terms through gloss classification. In: Proceedings of the 14th ACM international conference on Information and knowledge management (CIKM), 2005, pp. 617-624.
3. Wilson T, Wiebe J, Hoffmann P,. Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics 2009; 35 (3): 399-433.
4. Hu M, Liu B,. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, 2004, pp. 168-177.
5. Yang B, Cardie C,. Context-aware learning for sentence-level sentiment analysis with posterior regularization. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics (ACL), Baltimore, MD, 2014, pp. 325-335.
6. Devitt A, Ahmad K,. Sentiment polarity identification in financial news: A cohesion-based approach. In: Proceedings of the 45th annual meeting of the Association of Computational Linguistics (ACL), 2007, pp. 984-991.
7. Pang B, Lee L, Vaithyanathan S,. Thumbs up? Sentiment classification using machine learning techniques. In: Conference on empirical methods in natural language processing (EMNLP), Philadelphia, PA, 2002, pp. 79-86.
8. Yessenalina A, Yue Y, Cardie C,. Multi-level structured models for document-level sentiment classification. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010, pp. 1046-1056.
9. Phan X, Nguyen C, Le D, Nguyen L,. A hidden topic-based framework toward building applications with short Web documents. IEEE Transactions on Knowledge and Data Engineering 2011; 23 (7): 961-976.
10. Xu T, Peng Q,. Extended information inference model for unsupervised categorization of web short texts. Journal of Information Science 2012; 38 (6): 512-531.
11. Dai Z, Sun A, Liu X,. Crest: Cluster-based representation enrichment for short text classification. In: Advances in knowledge discovery and data mining. Berlin: Springer, 2013, pp. 256-267.
12. Hu X, Sun N, Zhang C, Chua T,. Exploiting internal and external semantics for the clustering of short texts using world knowledge. In: Proceedings of the 18th ACM conference on Information and knowledge management, 2009, pp. 919-928.
13. Shen D, Pan R, Sun J, Pan J,. Query enrichment for web-query classification. ACM Transactions on Information Systems 2006; 24 (3): 320-352.
14. Chen M, Jin X, Shen D,. Short text classification improved by learning multi-granularity topics. In: Proceedings of the twenty-second international joint conference on artificial intelligence (IJCAI), 2011, pp. 1776-1781.
15. Gabrilovich E, Markovitch S,. Harnessing the expertise of 70, 000 human editors: Knowledge-based feature generation for text categorization. Journal of Machine Learning Research 2007; 8 (10): 2297-2345.
16. Cai L, Zhou G, Liu K, Zhao J,. Large-scale question classification in cQA by leveraging Wikipedia semantic knowledge. In: Proceedings of the 20th ACM international conference on information and knowledge management, 2011, pp. 1321-1330.
17. Sahami M, Heilman T,. A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th international conference on World Wide Web, 2006, pp. 377-386.
18. Kennedy A, Inkpen D,. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 2006; 22 (2): 110-125.
19. Taboada M, Brooke J, Tofiloski M, Voll K,. Lexicon-based methods for sentiment analysis. Computational Linguistics 2011; 37 (2): 267-307.
20. Thelwall M, Buckley K, Paltoglou G, Cai D,. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 2010; 61 (12): 2544-2558.
21. Davidov D, Tsur O, Rappoport A,. Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd international conference on computational linguistics, 2010, pp. 241-249.
22. Gamon M, Aue A, Corston-Oliver S, Ringger E,. Pulse: Mining customer opinions from free text. In: Advances in intelligent data analysis VI, 2005, pp. 121-132. Berlin: Springer.
23. Saif H, He Y, Alani H,. Semantic sentiment analysis of twitter. In: 11th International Semantic Web conference, Boston, MA, 11-15 November 2012.
24. Montejo-Ráez A, Martínez-Cámara E, Martín-Valdivia M, Ureña-López L,. A knowledge-based approach for polarity classification in twitter. Journal of the Association for Information Science and Technology 2014; 65 (2): 414-425.
25. Blei DM, Ng AY, Jordan MI,. Latent dirichlet allocation. Journal of Machine Learning Research, 2003; 3: 993-1022.
26. Griffiths T, Steyvers M,. Finding scientific topics. The National Academy of Sciences 2004; 101: 5228-5235.
27. Lin C, He Y,. Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM conference on information and knowledge management, 2009, pp. 375-384.
28. Tang D, Wei F, Yang N, Zhou M,. Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics, Baltimore, MD, 2014, pp. 1555-1565.
29. Mikolov T, Chen K, Corrado G, Dean J,. Efficient estimation of word representations in vector space. In: Proceedings of workshop at ICLR, 2013.
30. Baroni M, Dinu G, Kruszewski G,. Don't count, predict! A systematic comparison of context-counting vs context-predicting semantic vectors. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics, Baltimore, MD, 2014, pp. 238-247.
31. Wan X,. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In: Proceedings of the conference on empirical methods in natural language processing, 2008, pp. 553-561.
32. Whitehead M, Yaeger L,. Sentiment mining using ensemble classification models. In: Innovations and Advances in Computer Sciences and Engineering. Berlin: Springer, 2010, pp. 509-514.
33. Pang B, Lee L,. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43th annual meeting of the Association of Computational Linguistics, 2005.
34. Maas A, Daly R, Pham P, Huang D,. Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics, 2011.
35. Levy O, Goldberg Y,. Dependency-based word embeddings. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics, Baltimore, MD, 2014, pp. 302-308.
36. Mohammad SM, 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'13), 2013.
37. McAuley J, Leskovec J,. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: Proceedings of the 22nd international conference on World Wide Web, 2013, pp. 897-908.