IIT-TUDA: System for sentiment analysis in Indian languages using lexical acquisition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9468, Issue , 2015, Pages 684-693

  Kumar, Ayush ^a   Kohail, Sarah ^b   Ekbal, Asif ^a   Biemann, Chris ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY PATNA   (India)

^b DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Cooccurrence; Distributional thesaurus; Indian languages; Sentiment analysis

Indexed keywords

DATA MINING; NATURAL LANGUAGE PROCESSING SYSTEMS; SOCIAL NETWORKING (ONLINE); THESAURI;

CO-OCCURRENCE; COMMUNICATION TOOLS; INDIAN LANGUAGES; LEXICAL ACQUISITION; NATURAL LANGUAGE PROCESSING; PARTICIPATING TEAMS; SENTIMENT ANALYSIS; SENTIMENT LEXICONS;

COMPUTATIONAL LINGUISTICS;

EID: 84955252399     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-26832-3_65     Document Type: Conference Paper

References (21)

1. Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. 26(3), 12:1–12:34 (2008)
2. Almatrafi, O., Parack, S., Chavan, B.: Application of location-based sentiment analysis using twitter for identifying trends towards indian general elections 2014. In: Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, pp. 41:1–41:5. IMCOM 2015 (2015)
3. Biemann, C., Riedl, M.: Text: Now in 2d! a framework for lexical expansion with contextual similarity. J. Lang. Model. 1(1), 55–95 (2013)
4. Biemann, C.: Unsupervised part-of-speech tagging in the large. Res. Lang. Comput. 7(2–4), 101–135 (2009)
5. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
6. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
7. Das, A., Bandyopadhyay, S.: Subjectivity detection in english and bengali: A crf-based approach. Proceeding of ICON, Hyderabad, India (2009)
8. Das, A., Bandyopadhyay, S.: Sentiwordnet for indian languages. Asian Federation for Natural Language Processing, China, pp. 56–63 (2010)
9. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)
10. Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural. Expert Syst. Appl. 40(16), 6266–6282 (2013)
11. Joshi, A., Balamurali, A., Bhattacharyya, P.: A fall-back strategy for sentiment analysis in hindi: a case study. Proceedings of the 8th ICON, Kharagpur, India (2010)
12. Miller, T., Biemann, C., Zesch, T., Gurevych, I.: Using distributional similarity for lexical expansion in knowledge-based word sense disambiguation. In: COLING, pp. 1781–1796 (2012)
13. Nagy, A., Stamberger, J.: Crowd sentiment detection during disasters and crises. In: Proceedings of the 9th International ISCRAM Conference, Vancouver, Canada, pp. 1–9 (2012)
14. O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets topolls: Linking text sentiment to public opinion time series. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC 11(122-129), pp. 1–2 (2010)
15. Panchenko, A., Beaufort, R., Naets, H., Fairon, C.: Towards detection of child sexual abuse media: categorization of the associated filenames. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 776–779. Springer, Heidelberg (2013)
16. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)
17. Patra, B.G., Das, D., Das, A., Prasath, R.: Shared task on sentiment analysis in indian languages (sail) tweets - an overview. In: Mining Intelligence and Knowledge Exploration - Third International Conference, MIKE-2015. Springer, Hyderabad, India (2015)
18. Quasthoff, U., Richter, M., Biemann, C.: Corpus portal for search in monolingual corpora. In: Proceedings of the Fifth International Conference on Language Resources and Evaluation, Genoa, Italy (2006)
19. Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S.M., Ritter, A., Stoyanov, V.: Semeval-2015 task 10: Sentiment analysis in twitter. In: Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval, Denver, Colorado (2015)
20. Rosenthal, S., Nakov, P., Ritter, A., Stoyanov, V.: Semeval-2014 task 9: Sentiment analysis in twitter. In: Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval, Dublin, Ireland (2014)
21. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. In: ICWSM, Washington, DC 10, pp. 178–185 (2010)