TwiSent: A multistage system for analyzing sentiment in Twitter

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 2531-2534

  Mukherjee, Subhabrata ^a   Malu, Akshat ^a   Balamurali, A R ^a,b   Bhattacharyya, Pushpak ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^b IITB MONASH RESEARCH ACADEMY   (India)

Author keywords

entity specific twitter sentiment; micro blogs; sentiment analysis; spam; twitter

Indexed keywords

DATA SETS; ENTITY SPECIFIC TWITTER SENTIMENT; EXISTING SYSTEMS; GOLD STANDARDS; MICRO-BLOG; MULTISTAGE SYSTEM; SENTIMENT ANALYSIS; SPAM; STRUCTURAL ANOMALY; TEXT GENRE; TWITTER;

CLASSIFICATION (OF INFORMATION); DATA MINING; INTERNET; KNOWLEDGE MANAGEMENT;

SOCIAL NETWORKING (ONLINE);

EID: 84871042282     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2398684     Document Type: Conference Paper

References (15)

1. Alec, G.; Lei, H.; and Richa, B. 2009. Twitter sentiment classification using distant supervision. Technical report, Stanford University.
2. Barbosa, L., and Feng, J. 2010. Robust sentiment detection on twitter from biased and noisy data. In Proceedings of the Computational Linguistics Posters, 36-44.
3. Joshi, A.; Balamurali, A. R.; Bhattacharyya, P.; and Mohanty, R. 2011. C-feel-it: a sentiment analyzer for microblogs. In Proc. of ACL Demo Papers, HLT '11
4. Bermingham, A., and Smeaton, A. 2010. Classifying sentiment in microblogs: Is Brevity an Advantage, ACM.
5. Raghunathan, K., and Krawczyk, S. 2009. Investigating sms text normalization using statistical machine translation. CS224NProject Report, Stanford University.
6. Church, K. W., and Gale, W. 1991. Probability scoring for spelling correction. Statistics and Computing 1(2):91-103.
7. Levenshtein, V. I. 1966. Binary codes capable of correcting deletions, insertions, and reversals. Technical Report 8.
8. Mukherjee, S., and Bhattacharyya, P. 2012. Feature specific sentiment analysis for product reviews. Part 1, Lecture Notes in Computer Science, Springer 7181:475-487.
9. Jindal, N. and Liu, B. 2008. Opinion spam and analysis. In Proceedings of the 2008 WSDM. pp. 219-229.
10. Liu, B., Lee, W., Yu S., and Li X. 2002. Partially supervised classification of text documents. In Proceedings of ICML.
11. Bieswanger, M. 2007. 2 abbrvi8 or not 2 abbrevi8: A contrastive analysis of different shortening strategies in English and german text messages. SALSA XIV.
12. Jonathon Read. 2005. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of the ACL Student Research Workshop.
13. Pak, Alexander and Paroubek, Patrick. 2010. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In Proceedings of the LREC.
14. Gonzalez-Ibanez, Roberto and Muresan, Smaranda and Wacholder, Nina. 2011. Identifying sarcasm in Twitter: a closer look, In Proceedings ofACL Short Papers.
15. In Wikipedia. Retrieved on August 11, 2012, from Website http://en.wikipedia.org/wiki/A/B-testing