The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena
Open accessAbstract
We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to extract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter content and compute a six-dimensional mood vector for each day in the timeline. We compare our results to a record of popular events gathered from media and sources. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.
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Cites
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,720
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
Self-citationPredicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T., Sandner, P., & Welpe, I. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,649
Why we twitter: understanding microblogging usage and communities
Java, A., Song, X., Finin, T., & Tseng, B. (2007) · Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis · doi:10.1145/1348549.1348556 · Cited by 2,645
McNair, D. M. (1992) · Medical Entomology and Zoology · Cited by 2,531
Pang, B., & Lee, L. (2005) · Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05 · doi:10.3115/1219840.1219855 · Cited by 2,129
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,923
Sentiment strength detection in short informal text
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449
A Shortened Version of the Profile of Mood States
Shacham, S. (1983) · Journal of Personality Assessment · doi:10.1207/s15327752jpa4703_14 · Cited by 1,231 · cited 1×
“A number of reduced versions have also appeared in specialized literature, with the aim to condense the number of test terms and thus to reduce the time and effort on the part of human subjects to complete the POMS questionnaire [23, 3].”
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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
Self-citationLerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010213-115043 · Cited by 2,913
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior
Stieglitz, S., & Dang-Xuan, L. (2013) · Journal of Management Information Systems · doi:10.2753/mis0742-1222290408 · Cited by 1,604
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
- Bollen, J., Mao, H., & Pepe, A.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 5
- Issue
- 1
- Pages
- 450-453
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W359818833
- Cited by
- 936
- References
- 25
- FWCI
- 18.63
- Type
- conference paper
- Language
- en
- Open access
- diamond
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
