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Issue No.02 - March-April (2013 vol.28)
pp: 15-21
Erik Cambria , National University of Singapore
Bjorn Schuller , Technical University of Munich
Yunqing Xia , Tsinghua University
Catherine Havasi , Massachusetts Institute of Technology
ABSTRACT
The distillation of knowledge from the Web—also known as opinion mining and sentiment analysis—is a task that has recently raised growing interest for purposes such as customer service, predicting financial markets, monitoring public security, investigating elections, and measuring a health-related quality of life. This article considers past, present, and future trends of sentiment analysis by delving into the evolution of different tools and techniques—from heuristics to discourse structure, from coarse- to fine-grained analysis, and from keyword- to concept-level opinion mining.
INDEX TERMS
Semantics, Pragmatics, Intelligent systems, Natural language processing, Context awareness, Market research, Data mining, Information analysis, Intelligent systems, Knowledge discovery, Natural language processing, sentiment analysis, Semantics, Pragmatics, Intelligent systems, Natural language processing, Context awareness, Market research, Data mining, Information analysis, Intelligent systems, Knowledge discovery, Natural language processing, intelligent systems, AI, NLP, opinion mining
CITATION
Erik Cambria, Bjorn Schuller, Yunqing Xia, Catherine Havasi, "New Avenues in Opinion Mining and Sentiment Analysis", IEEE Intelligent Systems, vol.28, no. 2, pp. 15-21, March-April 2013, doi:10.1109/MIS.2013.30
REFERENCES
1. B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval, vol. 2, nos. 1–2, 2008, pp. 1–135.
2. B. Liu, Sentiment Analysis and Opinion Mining, Morgan and Claypool, 2012.
3. E. Cambria and A. Hussain, Sentic Computing: Techniques, Tools, and Applications, Springer, 2012.
4. V. Hatzivassiloglou and K. McKeown,“Predicting the Semantic Orientation of Adjectives,” Proc. 8th Conf. Assoc. Computational Linguistics European Chapter, 1997.
5. A. Popescu and O. Etzioni, “Extracting Product Features and Opinions from Reviews,” Proc. Human Language Technology Conf./Conf. Empirical Methods in Natural Language Processing, Assoc. for Computational Linguistics, 2005, pp. 339–346.
6. B. Snyder and R. Barzilay, “Multiple Aspect Ranking Using the Good Grief Algorithm,” Proc. Ann. Conf. North Am. Chapter of the Assoc. for Computational Linguistics, Assoc. for Computational Linguistics, 2007, article no. N07- 1038.
7. B. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts,” Proc. 42nd Ann. Meeting of the Assoc. for Computational Linguistics, Assoc. for Computational Linguistics, 2004, pp. 271–278.
8. M. Joshi and C. Penstein-Rosé, “Generalizing Dependency Features for Opinion Mining,” Proc. 47th Ann. Meeting of the Assoc. for Computational Linguistics and the 4th Int'l Joint Conf. Natural Language Processing of the Asian Federation of Natural Language Processing, Assoc. for Computational Linguistics, 2009, pp. 313–316.
9. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs Up? Sentiment Classification Using Machine Learning Techniques,” Proc. Ann. Conf. Empirical Methods in Natural Language Processing, Assoc. for Computational Linguistics, 2002, pp. 79–86.
10. B. Pang and L. Lee, “Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales,” Proc. 43rd Ann. Assoc. for Computational Linguistics, Assoc. for Computational Linguistics, 2005, pp. 115–124.
11. P. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews,” Proc. 40th Ann. Assoc. for Computational Linguistics, Assoc. for Computational Linguistics, 2002, pp. 417–424.
12. J. Kamps et al., “Using WordNet to Measure Semantic Orientation of Adjectives,” Proc. 4th Ann. Int'l. Conf. Language Resources and Evaluation, European Language Resources Assoc., 2004, pp. 1115–1118.
13. E. Riloff and J. Wiebe, “Learning Extraction Patterns for Subjective Expressions,” Proc. 2003 Conf. Empirical Methods in Natural Language Processing, Assoc. for Computational Linguistics, 2003, pp. 105–112.
14. S. Kim and E. Hovy, “Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text,” Proc. Workshop on Sentiment and Subjectivity in Text, Assoc. for Computational Linguistics, 2006, pp. 1–8.
15. M. Hu and B. Liu, “Mining and Summarizing Customer Reviews,” Proc. 10th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, ACM, 2004, pp. 168–177.
16. B. Lu et al., “Multi-Aspect Sentiment Analysis with Topic Models,” Proc. Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, IEEE CS, 2011, pp. 81–88.
17. G. Di Fabbrizio,A. Aker, and R. Gaizauskas, “STARLET: Multi-Document Summarization of Service and Product Reviews with Balanced Rating Distributions,” Proc. Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, IEEE, 2011, pp. 67–74.
18. C.D. Elliott, “The Affective Reasoner: A Process Model of Emotions in a Multi-Agent System,” doctoral dissertation, Institute for the Learning Sciences, Northwestern University, 1992.
19. A. Ortony, G. Clore, and A. Collins, The Cognitive Structure of Emotions, Cambridge Univ. Press, 1988.
20. J. Wiebe, T. Wilson, and C. Cardie, “Annotating Expressions of Opinions and Emotions in Language,” Language Resources and Evaluation, vol. 39, no. 2, 2005, pp. 165–210.
21. R. Stevenson, J. Mikels, and T. James, “Characterization of the Affective Norms for English Words by Discrete Emotional Categories,” Behavior Research Methods, vol. 39, no. 4, 2007, pp. 1020–1024.
22. S. Somasundaran, J. Wiebe, and J. Ruppenhofer, “Discourse Level Opinion Interpretation,” Proc. 22nd Int'l Conf. Computational Linguistics, Assoc. for Computational Linguistics, 2008, pp. 801–808.
23. D. Rao and D. Ravichandran, “Semi-Supervised Polarity Lexicon Induction,” Proc. 12th Conf. European Chapter of the Assoc. for Computational Linguistics, Assoc. for Computational Linguistics, 2009, pp. 675–682.
24. L. Nguyen et al., “Predicting Collective Sentiment Dynamics from Time-Series Social Media,” Proc. 18th ACM SIGKDD Conf. Knowledge Discovery and Data Mining, ACM, 2012, article no. 6.
25. M. Grassi et al., “Sentic Web: A New Paradigm for Managing Social Media Affective Information,” Cognitive Computation, vol. 3, no. 3, 2011, pp. 480–489.
26. D. Olsher, “Full Spectrum Opinion Mining: Integrating Domain, Syntactic and Lexical Knowledge,” Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, IEEE, 2012, pp. 693–700.
27. E. Cambria, T. Mazzocco, and A. Hussain, “Application of Multi-Dimensional Scaling and Artificial Neural Networks for Biologically Inspired Opinion Mining,” Biologically Inspired Cognitive Architectures, vol. 4, 2013, pp. 41–53.
28. B. Schuller et al., “Recognizing Realistic Emotions and Affect in Speech: State of the Art and Lessons Learnt from the First Challenge,” Speech Comm., vol. 53, nos. 9/10, 2011, pp. 1062–1087.
29. H. Gunes and B. Schuller, “Categorical and Dimensional Affect Analysis in Continuous Input: Current Trends and Future Directions,” Image and Vision Computing, vol. 31, no. 2, 2012, pp. 120–135.
30. S. Raaijmakers, K. Truong, and T. Wilson, “Multimodal Subjectivity Analysis of Multiparty Conversation,” Proc. Conf. Empirical Methods in Natural Language Processing, Assoc. for Computational Linguistics, 2008, pp. 466–474.
31. L.-P. Morency, R. Mihalcea, and P. Doshi, “Towards Multimodal Sentiment Analysis: Harvesting Opinions from the Web,” Proc. 13th Int'l Conf. Multimodal Interfaces, ACM, 2011, pp. 169–176.
32. E. Cambria et al., “Big Social Data Analysis,” Big Data Computing, ch. 13, Chapman and Hall/CRC, 2013.
33. E. Cambria et al., “Semantic Multi-Dimensional Scaling for Open-Domain Sentiment Analysis,” IEEE Intelligent Systems, preprint, 2012; doi:10.1109/MIS.2012.118.
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