Adam Bermingham
ABSTRACT
Microblogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. However, this short length coupled with their noisy nature can pose difficulties for standard machine learning document representations. In this work we examine the hypothesis that it is easier to classify the sentiment in these short form documents than in longer form documents. Surprisingly, we find classifying sentiment in microblogs easier than in blogs and make a number of observations pertaining to the challenge of supervised learning for sentiment analysis in microblogs.
- S. Agarwal, S. Godbole, D. Punjani, and S. Roy. How much noise is too much: A study in automatic text classification. In ICDM, pages 3--12, 2007. Google Scholar
Digital Library
- J. Bollen, A. Pepe, and H. Mao. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. CoRR, abs/0911.1583, 2009.Google Scholar
- P. Carvalho, L. Sarmento, M. J. Silva, and E. de Oliveira. Clues for detecting irony in user-generated contents: oh...!! it's "so easy" ;-). In TSA '09: Proceeding of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion, pages 53--56, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- M. Choudhury, R. Saraf, V. Jain, A. Mukherjee, S. Sarkar, and A. Basu. Investigation and modeling of the structure of texting language. IJDAR, 10(3--4):157--174, 2007. Google ScholarDigital Library
- N. A. Diakopoulos and D. A. Shamma. Characterizing debate performance via aggregated Twitter sentiment. In Conference on Human Factors in Computing Systems (CHI 2010), 2010. Google ScholarDigital Library
- A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for opinion mining. In In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC-06), pages 417--422, 2006.Google Scholar
- M. Gamon. Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In COLING '04: Proceedings of the 20th international conference on Computational Linguistics, page 841, Morristown, NJ, USA, 2004. Association for Computational Linguistics. Google ScholarDigital Library
- C. MacDonald and I. Ounis. The TREC Blogs06 collection: Creating and analysing a blog test collection. Technical report, University of Glasgow, Department of Computing Science, 2006.Google Scholar
- S. Matsumoto, H. Takamura, and M. Okumura. Sentiment classification using word sub-sequences and dependency sub-trees. In Proceedings of PAKDD'05, the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2005. Google ScholarDigital Library
- N. O'Hare, M. Davy, A. Bermingham, P. Ferguson, P. Sheridan, C. Gurrin, and A. F. Smeaton. Topic-dependent sentiment analysis of financial blogs. In In: TSA 2009 - 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, Hong Kong, China, 6 Nov 2009. Google ScholarDigital Library
- B. Pang and L. Lee. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, page 271, Morristown, NJ, USA, 2004. Association for Computational Linguistics. Google ScholarDigital Library
- B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundation and Trends in Information Retrieval, 2(1--2):1--135, 2008. Google ScholarDigital Library
- S. A. Tagliamonte and D. Denis. LINGUISTIC RUIN? LOL! INSTANT MESSAGING AND TEEN LANGUAGE. American Speech, 83(1):3--34, 2008.Google Scholar
- T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 347--354, 2005. Google ScholarDigital Library
Index Terms
- Classifying sentiment in microblogs: is brevity an advantage?
Recommendations
Sentiment Analysis on Microblogs for Natural Disasters Management: a Study on the 2014 Genoa Floodings
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide WebPeople use social networks for different communication purposes, for example to share their opinion on ongoing events. One way to exploit this common knowledge is by using Sentiment Analysis and Natural Language Processing in order to extract useful ...
Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog
As a new form of social media, microblogging provides platform sharing, wherein users can share their feelings and ideas on certain topics. Bursty topics from microblogs are the results of the emerging issues that instantly attract more followers and ...
Detecting bursts in sentiment-aware topics from social media
Nowadays plenty of user-generated posts, e.g., sina weibos, are published on the social media. The posts contain the publics sentiments (i.e., positive or negative) towards various topics. Bursty sentiment-aware topics from these posts reveal sentiment-...
Comments