Bengali Wikipedia 10th Anniversary Celebration Kolkata/Submissions/Survey of Sentiment Analysis

Submission no.

Title of the submission

Survey of Sentiment Analysis

Type of submission (discussion, hot seat, panel, presentation, tutorial, workshop)

Author of the submission

Anusuya

E-mail address

Username

Country of origin

India

Affiliation, if any (organisation, company etc.)

Personal homepage or blog

Abstract (at least 300 words to describe your proposal)

In recent days, Sentiment Analysis is studied and analyzed by divergence, and collaboration ways to model a framework for determining sentiment. In this regard, a lot of attempts have customized scopes and purposes. Sentiment Analysis gains momentum due to numerous applications are revealed in the real world as well as associated significant return on investment [10]. Researchers across the globe applied ranges of methods over periods of time that slowly but steadily betterment the extraction procedure of sentiment and its associated evaluations. However, a generic framework for computation of sentiment has hardly been achieved till now. Few interesting survey papers [11, 12, 13] are found that cited papers latest by year 2013. Substantial advancement is incurred thereafter with new approaches, cutting-edge scopes, and etc. In this work, more than thirty research papers are studied. Among those, five novel and utmost reputed research works [1-5] are classified according to their approaches, comparison studies of results with others, and preparation of experimental data set. Summarization, Tables, and diagram are used to illustrate pros and cons of different approaches in the survey ork.

Reference

1. M. Munezero, C. S. Montero, E. Sutinen, J. Pajunen, “Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text,” IEEE Trans. on Affective Computing, vol. 5, no. 2, pp. 101-111, Apr.-Jun. 2014.
2. A. Neviarouskaya, H. Prendinger, M. Ishizuka, “SentiFul: A Lexicon for Sentiment Analysis,” IEEE Trans. on Affective Computing, vol. 2, no. 1, pp. 22 – 36, Jan-Mar 2011.
3. U. Krcadinac, P. Pasquier, J. Jovanovic, V. Devedzic, “Synesketch: An Open Source Library for Sentence-Based Emotion Recognition,” IEEE Trans. on Affective computing, vol. 4, no. 3, pp. 312 - 325, Sep. 2013.
4. D. Das, S. Bandyopadhyay, “Emotion Co-referencing -Emotional Expression, Holder, and Topic,” In Int’l Journal of Computational Linguistics and Chinese Language Processing(IJCLCLP), vol. 18, no. 1, pp. 79-98, Mar. 2013.
5. J. Cao, K. Zeng, H. Wang, J. Cheng, F. Qiao, D. Wen, Y. Gao, “Web-Based Traffic Sentiment Analysis: Methods and Applications, ” IEEE Trans. on Intelligent Transportation System, vol.15, no. 2, pp. 844 – 853, Apr. 2014.
6. F. Benamara, C. Cesarano, D. Reforgiato, “Sentiment Analysis: Adverbs and Adjectives Are Better than Adverbs Alone,” in Proc. 2007 Int’l Conf. Weblogs and Social Media (ICWSM 07), 2007; www.icwsm.org/papers/3--Benamara-Cesarano-Picariello-Reforgiato-Subrahmanian .pdf.
7. V.S. Subrahmanian, D. Reforgiato, “AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis,” in Intelligent Systems, IEEE, vol. 23, no. 4, pp. 43 – 50, Jul-Aug 2008.
8. A. Kumar and T. M. Sebastian, “Sentiment Analysis on Twitter,” in Int’l Journal of Computer Science Issues(IJCSI), vol. 9, issue 4, no. 3, Jul 2012, ISSN (Online): 1694-0814, www.IJCSI.org.
9. R. Batool, A. M. Khattak, J. Maqbool and S. Lee, “Precise Tweet Classification and Sentiment Analysis,” in Proc. 12th Int’l Conf. Computer and Information Science (ICIS), pp. 461 – 466, Jun 2013.
10. N. Stefan, K. Jonas, S. Detlef, “ Predictive Analytics On Public Data – The Case Of Stock Markets,” in Proc. 21st European Conf. on Information Systems, Jun 2013.
11. H. Tang, S. Tan, X Cheng, “A survey on sentiment detection of reviews,” in Int’l Journal of Expert Systems with Applications 36 (2009), pp. 10760–10773, journal homepage: www.elsevier.com/locate/eswa.
12. G. Vinodhini, R. M. Chandrasekaran, “ Sentiment analysis and opinion Mining: A Survey,” Int’l Journal of Advanced Research in Computer Science and Software Engineering , vol. 2, no. 6 pp. 282-292, Jun 2012.
13. W. Medhat, A. Hassan, H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, accepted Apr 2014.
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25. A. Neviarouskaya, H. Prendinger, and M. Ishizuka, “Textual Affect Sensing for Sociable and Expressive Online Communication,” Proc. Second Int’l Conf. Affective Computing and Intelligent Interaction, pp. 220-231, 2007.
26. D. Das, & S. Bandyopadhyay, “Labeling Emotion in Bengali Blog Corpus – A Fine Grained Tagging at Sentence Level,” in Proc. 23rd Int’l Conf. on Computational Linguistics , COLING-2010, pp. 47-55, Beijing, China, 2010.
27. D. Das, & S. Bandyopadhyay, “Developing Bengali WordNet Affect for Analyzing Emotion,” in Proc. 23rd Int’l Conf. on Computer Processing of Oriental Languages (ICCPOL-2010), pp. 35-40, California, USA, 2010.
28. K. Krippendorff, “Computing Krippendorff's Alpha-Reliability,” University of Pennsylvania, 2011.
29. W. Che, Z. Li, and T. Liu, “LTP: A Chinese language technology platform,” in Proc. 23rd Int’l. Conf. Comput. Linguist., Demo., pp.13–16, 2010.
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33. T. Nasukawa and J. Yi, “Sentiment analysis: Capturing favorability using natural language processing,” in Proc. 2nd Int’l Conf. on Knowledge capture. ACM, 2003, pp. 70–77.
34. J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack, “Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques,” in Proc. Third IEEE Int’l Conf. on Data Mining (ICDM 2003), pp. 427–434, 2003.
35. N. Godbole, M. Srinivasaiah, S. Skiena, “Large-scale sentiment analysis for news and blogs,” in Proc. Int’l Conf. on Weblogs and Social Media (ICWSM), pp. 219– 222, 2007.
36. B. J. Jansen, M. Zhang, K. Sobel, A. Chowdury, “Twitter power: Tweets as electronic word of mouth,” J. of the American society for Information Science and Technology, vol. 60, no. 11, pp. 2169–2188, 2009.
37. B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, M. Demirbas, “Short text classification in twitter to improve information filtering,” in Proc. 33rd Int’l ACM SIGIR Conf. on research and development in information retrieval, pp. 841–842, ACM, 2010.
38. P. Turney, “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews,” Proc. 40th Ann. Meeting Assoc. for Computational Linguistics (ACL 02), Assoc. for Computational Linguistics, pp.417−424, 2002.
39. S.O. Kim and E. Hovy, “Determining the Sentiment of Opinions,” Proc. 20th Int’l Conf. Computational Linguistics (COLING04), pp. 1367−1373, ACM Press, 2004.
40. L.-W. Ku, Y.-T. Liang, and H.-H. Chen, “Opinion extraction, summarization and tracking in news and blog corpora,” in Proc. AAAI Spring Symp., Comput. Approaches Anal. Weblogs, pp. 100–107, 2006.

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