IJRCS – Volume 3 Issue 4 Paper 2

Posted on by Researchscripteditor

AN OVERVIEW OF SENTIMENT ANALYSIS: APPROACHES AND APPLICATIONS

Author’s Name : Sunayana Bhandari | Dr Subhajit Ghosh 

Volume 03 Issue 04  Year 2016  ISSN No:  2349-3828  Page no: 4-7

12

Abstract:

Sentiment analysis is a recent area of research that deals with interpreting user sentiments in web articles, tweets, blog post, product review and news reports. It divides the data based on its polarity i.e. positive, negative or neutral. These sentiments are used by organizations to understand user point of views and improve business performance.  This survey paper highlights the fundamentals of sentiment analysis, various sentiment analysis approaches and methodologies developed and used so far; and its various areas of applications. It compares sentiment analysis with certain other data analysis techniques.

Keywords:

Sentiment Analysis; Supervised sentiment analysis; Semi supervised sentiment analysis; Unsupervised sentiment analysis; Coarse grained Sentiment Analysis;  Fine grained sentiment analysis

References:

  1. Rudy Prabowo, and Mike Thelwall, “Sentiment analysis: A combined approach.”, Journal of Informetrics 3 (2009) 143–157
  2. Edoardo Airoldi, E., Cohen, and W., Fienberg, “S.: Bayesian models for frequent terms in text” (manuscript, 2005)
  3. Edoardo Airoldi, Bai, and R. Padman, “Markov blankets and meta-heuristic search: Sentiment extraction from unstructured text,” Lecture Notes in Computer Science, vol. 3932, pp. 167–187, 2006.
  4. J. R. Quinlan, “Induction of decision trees.”, Machine Learning, 1, (1986). 81
  5. W. W. Cohen, “Fast effective rule induction.”, A. Prieditis & S. Russell (Eds.), Proceedings of the 12th international conference on machine learning (ICML 1995), July 9–12, 1995 (pp. 115 123). Tahoe City, California, USA.
  6. T. Joachims, “Making large-scale SVM learning practical.”, B. Sch¨olkopf, C. J. C. Burges, & A. J. Smola (Eds.), Advances in kernel methods: support vector learning. (1998). The MIT Press.
  7. Zhi Liu, Sanya Liu, Lin Liu, Jianwen Sun, Xian Peng and Tai Wang, “Sentiment recognition of online course reviews using multi-swarm optimization based selected features”, Neurocomputing, 2015. 12.036
  8. Andrea Esuli, and Fabrizio Sebastiani, “Determining the semantic orientation of terms through gloss classification.” ,Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, 2005.
  9. Q.V. Li, T. Mikolov, “Distributed representations of sentences and documents”, arXiv preprint arXiv: 1405.4053, 2014
  10. Zhijian Cui, Xiaodong Shi and Yidong Chen, “Sentiment Analysis via Integrating Distributed Representations of Variable-length Word Sequence”, Neurocomputing, 2015.07.129
  11. Chunxu Wu, Lingfeng Shen, and Xuan Wang, “A New Method of Using Contextual Information to Infer the Semantic Orientations of Context Dependent Opinions”, International Conference on Artificial Intelligence and Computational Intelligence, 2009.
  12. ]B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” Proc. EMNLP’02, 2002, pp. 79–86.
  13. Ryan McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeff Reynar. 2007. Structured models for fine-to-coarse sentiment analysis. In Proceedings of the Annual Conference of the Association for Computational Linguistics (ACL).
  14. Oscar Tackstrom, and Ryan McDonald, “Semi-supervised latent variable models for sentence-level sentiment analysis”, The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers – Volume 2, 2011, Pages 569-574
  15. Marjan Van de Kauter, Diane Breesch, and Véronique Hoste, “Fine-grained analysis of explicit and implicit sentiment in financial news Articles”, Expert Systems with Applications 42 (2015) 4999–5010
  16. Xiaolong Wang, et al. “Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach.” Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 2011.
  17. Thien Hai Nguyen, Kiyoaki Shirai, and Julien Velcin, “Sentiment analysis on social media for stock movement prediction”, Expert Systems With Applications 42 (2015) 9603–9611
  18. Justin Martineau, and Tim Finin “Delta TFIDF: An Improved Feature Space for Sentiment Analysis”, Proceedings of the Third International ICWSM Conference (2009)
  19. Mohamed Abdel Fattah, “New term weighting schemes with combination of multiple classifiers for sentiment analysis”, Neurocomputing 167 (2015) 434–442
  20. Wanxiang Che, Yanyan Zhao, Honglei Guo, Zhong Su, and Ting Liu, “Sentence Compression for Aspect-Based Sentiment Analysis”, ieee/acm transactions on audio, speech, and language processing, vol. 23, no. 12, december 2015
  21. S. M. Mohammad, S. Kiritchenko, and X. Zhu, “NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets,” CoRR, vol. abs/1308.6242, 2013.
  22. Mohammad Sadegh Hajmohammadi, Roliana Ibrahim, Ali Selamat, and Hamido Fujita, “Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples”, Information Sciences 317 (2015) 67–77
  23. Parisa Lak, and Ozgur Turetken, “Star Ratings versus Sentiment Analysis – A Comparison of Explicit and Implicit Measures of Opinions”, 47th Hawaii International Conference on systescience, 2014
  24. Khalid Al-Rowaily, Muhammad Abulaish, Nur Al-Hasan Haldar, and Majed Al-Rubaian, “BiSAL e A bilingual sentiment analysis lexicon to analyze. Dark Web forums for cyber security”, Digital Investigation 14 (2015) 53e62
  25. E. Diener, “Subjective well-being”, Psychol. Bull. 95, 1984, pp. 542–575.
  26. E. Diener, “Subjective well-being: the science of happiness and a proposal for a national index”, Am. Psychol. 55 (1), 2000, p. 34.
  27. Jiayin Qi, Xianglin Fu, and Ge Zhu “Subjective well-being measurement based on Chinese grassroots blog text sentiment analysis”, Information & Management 52 (2015) 859–869