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Twitter as a Corpus for Sentiment Analysis and Opinion Mining
Open accessAbstract
Microblogging today has become a very popular communication tool among Internet users.Millions of users share opinions on different aspects of life everyday.Therefore microblogging web-sites are rich sources of data for opinion mining and sentiment analysis.Because microblogging has appeared relatively recently, there are a few research works that were devoted to this topic.In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis.We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes.We perform linguistic analysis of the collected corpus and explain discovered phenomena.Using the corpus, we build a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document.Experimental evaluations show that our proposed techniques are efficient and performs better than previously proposed methods.In our research, we worked with English, however, the proposed technique can be used with any other language.
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Cited by
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
“Over the past 5 years significant progress has been made in sentiment tracking techniques that extract indicators of public mood directly from social media content such as blog content [10], [12], [15], [17] and in particular large-scale Twitter feeds [22].”
BackgroundBig Data: Astronomical or Genomical?
Stephens, Z. D., Lee, S. Y., Faghri, F., Campbell, R. H., Zhai, C., Efron, M. J., Iyer, R., Schatz, M. C., Sinha, S., & Robinson, G. E. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002195 · Cited by 1,340
Understanding Social Media Logic
Van Dijck, J., & Poell, T. (2013) · Media and Communication · doi:10.17645/mac.v1i1.70 · Cited by 1,258
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181
Sentiment strength detection for the social web
Thelwall, M., Buckley, K., & Paltoglou, G. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21662 · Cited by 1,062
Metadata
- Authors
- Pak, A., & Paroubek, P.
- Venue
- Language Resources and Evaluation Conference (LREC)
- Published
- 2010-05-01
- OpenAlex
- W40549020
- Cited by
- 2,704
- References
- 0
- FWCI
- 86.02
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
