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Twitter Sentiment Analysis: The Good the Bad and the OMG!
Open accessAbstract
In this paper, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervied approach to the problem, but leverage existing hashtags in the Twitter data for building training data.
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Citations
Cited by
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
Ghiassi, M., Skinner, J., & Zimbra, D. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.057 · Cited by 485
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M., & Benevenuto, F. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0085-1 · Cited by 468
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter
Saif, H., Fernandez, M., He, Y., & Alani, H. (2014) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/3uepwmfe264a · Cited by 219
Naseem, U., Razzak, I., Khan, S. K., & Prasad, M. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3434237 · Cited by 194
Visualizing Student Opinion Through Text Analysis
Cunningham-Nelson, S., Baktashmotlagh, M., & Boles, W. (2019) · IEEE Transactions on Education · doi:10.1109/te.2019.2924385 · Cited by 75
Lampropoulos, G., Ferdig, R. E., & Kaplan-Rakowski, R. (2023) · doi:10.2139/ssrn.4468181 · Cited by 33
Jeong, H., Yoo, J. H., & Goh, M. (2023) · 2023 IEEE International Conference on Agents (ICA) · doi:10.1109/ica58824.2023.00019 · Cited by 2
Metadata
- Authors
- Kouloumpis, E., Wilson, T., & Moore, J.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 5
- Issue
- 1
- Pages
- 538-541
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2267835966
- Cited by
- 1,225
- References
- 0
- FWCI
- 27.27
- Type
- conference paper
- Language
- en
- Open access
- diamond
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
