A Review on the Research of Sentiment Analysis in Computational Linguistics

Abstract:

This paper present a review on the research of sentiment analysis in computational linguistics (CL). This information can contribute to defining the reference point for appraisal in CL. Some different approaches to related problems in documents are also be discussed with the aim of formulating the research issues.

• Periodical: Applied Mechanics and Materials (Volumes 58-60)
• Pages: 1636-1641
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.58-60.1636
• Online since: June 2011
• Authors: Jian Xiong Wang, Yuan Yong Feng, Fu Fang Li
• Keywords: Appraisal, Computational Linguistics, Sentiment Analysis

References

[1] P.D. Turney, and M.L. Littman, Measuring praise and criticism: Inference of semantic orientation from association. ACM Transaction on Information System 21 (2003) 315-346.

DOI: 10.1145/944012.944013

[2] P.D. Turney, Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, (2001).

DOI: 10.3115/1073083.1073153

[3] B. Pang, L. Lee, and S. Vaithyanathan, Thumbs up? Sentiment Classification using Machine Learning Techniques, 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, University of Pennsylvania, Philadelphia, PA, USA, (2002).

DOI: 10.3115/1118693.1118704

[4] J.R. Martin, Beyond Exchange: APPRAISAL Systems in English. in: S. Hunston, and G. Thompson, (Eds. ), Evaluation in Text: Authorial Stance and the Construction of Discourse, Oxford University Press, Oxford, (2000), pp.142-175.

DOI: 10.1177/14614456020040041104

[5] V. Hatzivassiloglou, and K.R. McKeown, Predicting the Semantic Orientation of Adjectives, the eighth conference on European chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Madrid, Spain, (1997).

DOI: 10.3115/979617.979640

[6] Kenneth W. Church, and P. Hanks, Word association norms, mutual information, and Lexicography, the 27th. Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Vancouver, B.C., (1989), pp.76-83.

DOI: 10.3115/981623.981633

[7] J.R. Firth, A synopsis of linguistic theory 1930-55, Studies in Linguistic Analysis (special volume of the Philological Society), The Philological Society, Oxford, (1957), pp.1-32.

[8] P.D. Turney, Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL, EMCL '01: Proceedings of the 12th European Conference on Machine Learning, Springer-Verlag, London, (2001), pp.491-502.

DOI: 10.1007/3-540-44795-4_42

[9] P.D. Turney, and M.L. Littman, Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus, Institute for Information Technology, National Research Council Canada, (2002).

[10] C. Whitelaw, N. Garg, and S. Argamon, Using appraisal groups for sentiment analysis, Proceedings of the 14th ACM international conference on Information and knowledge management, ACM, New York, (2005), pp.625-631.

DOI: 10.1145/1099554.1099714

[11] S. -M. Kim, and E. Hovy, Determining the sentiment of opinions, the 20th international conference on Computational Linguistics, Association for Computational Linguistics, Geneva, Switzerland, (2004).

DOI: 10.3115/1220355.1220555

[12] B. Pang, and L. Lee, Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr. 2 (2008) pp.1-135.

[13] D.D. Lewis, Naïve (Bayes) at forty: The independence assumption in information retrieval., the European Conference on Machine Learning (ECML), Springer Verlag, (1998), pp.4-15.

DOI: 10.1007/bfb0026666

[14] K. Toutanova, and C.D. Manning, Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. in: H. Schütze, and K. -Y. Su, (Eds. ), Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora, Association for Computational Linguistics, Morristown, NJ, (2000).

DOI: 10.3115/1117794.1117802

[15] T. Joachims, Learning to classify text using support vector machines, Kluwer Academic Publishers, Boston, (2002).

DOI: 10.1007/978-1-4615-0907-3_3

[16] M.A.M. Shaikh, H. Prendinger, and I. Mitsuru, Assessing Sentiment of Text by Semantic Dependency and Contextual Valence Analysis. in: A. Paiva, R. Prada, and R.W. Picard, (Eds. ), Affective Computing and Intelligent Interaction, Springer-Verlag Berlin Heidelberg, Berlin, (2007).

DOI: 10.1007/978-3-540-74889-2_18

[17] M. Drezde, J. Blitzer, and F. Pereira, Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification, the 45th Annual Meeting of the Association for Computational Linguistics, Prague, CZ, (2007).

[18] A. Andreevskaia, and S. Andreevskaia, When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging, ACL-08: HLT, Association for Computational Linguistics, Columbus, Ohio, (2008), pp.290-298.

[19] A. Aue, and M. Gamon, Customizing sentiment classifiers to new domains: a case study, the International Conference on Recent Advances in Natural Language Processing, Borovets, BG., (2005).

[20] A. Oak, Performing architecture: Talking architect' and 'client, into being CoDesign: International Journal of CoCreation in Design and the Arts 5 (2009) pp.51-63.

DOI: 10.1080/15710880802518054

[21] J. McDonnell, Collaborative negotiation in design: A study of design conversations between architect and building users. CoDesign: International Journal of CoCreation in Design and the Arts 5 (2009) 35-50.

DOI: 10.1080/15710880802492862

[22] L.W. Ku, Y. S. Lo, and H. H. Chen, Test Collection Selection and Gold Standard Generation for a Multiply-Annotated Opinion Corpus, the 45th Annual Meeting of the Association for Computational Linguistics, The Association for Computer Linguistics, Prague, Czech Republic, (2007).

DOI: 10.3115/1557769.1557796

[23] J. Wiebe, T. Wilson, and C. Cardie, Annotating expressions of opinions and emotions in language. Language Resources and Evaluation (formerly Computers and the Humanities) 39 (2005) 164-210.

DOI: 10.1007/s10579-005-7880-9

[24] K. Krippendorff, Content Analysis: an introduction to its methodology, SAGE Publications, Thousand Oaks, CA, (1980).

[25] R. Craggs, and M.M. Wood, Evaluating Discourse and Dialogue Coding Schemes. Comput. Linguist. 31 (2005), pp.289-296.

DOI: 10.1162/089120105774321109

[26] R. Artstein, and M. Poesio, Inter-coder agreement for computational linguistics. Comput. Linguist. 34 (2008), pp.555-596.

DOI: 10.1162/coli.07-034-r2

[27] T. Mullen, and R. Malouf, A Preliminary Investigation into Sentiment Analysis of Informal Political Discourse, AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW 2006), (2006), pp.159-162.

[28] M. Bansal, C. Cardie, and L. Lee, The power of negative thinking: Exploiting label disagreement in the min-cut classification framework, the International Conference on Computational Linguistics(COLING), (2008).

[29] A. Ahmed, C. Hsinchun, and S. Arab, Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums. ACM Trans. Inf. Syst. 26 (2008), pp.1-34.

DOI: 10.1145/1361684.1361685