Sentiment analysis on evolving social streams: how self-report imbalances can help
Abstract
Real-time sentiment analysis is a challenging machine learning task, due to scarcity of labeled data and sudden changes in sentiment caused by real-world events that need to be instantly interpreted. In this paper we propose solutions to acquire labels and cope with concept drift in this setting, by using findings from social psychology on how humans prefer to disclose some types of emotions. In particular, we use findings that humans are more motivated to report positive feelings rather than negative feelings and also prefer to report extreme feelings rather than average feelings.
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Users are more likely to talk about extreme or positive experiences than common or negative experiences.
“Further, users are more likely to talk about extreme or positive experiences than common or negative experiences”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Guerra et al., 2014
User demographic composition and participation on a specific topic or interaction patterns are often non-stationary.
“Even the user demographic composition and participation on a specific topic (Guerra et al., 2014; Diaz et al., 2016) or their interaction patterns (Viswanath et al., 2009) are often non-stationary.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Guerra et al., 2014
Cites
Ryan, T., & Xenos, S. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2011.02.004 · Cited by 1,260 · see citing works
Meshi, D., Morawetz, C., & Heekeren, H. R. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00439 · Cited by 181 · see citing works
Representation and communication: challenges in interpreting large social media datasets
Rost, M., Barkhuus, L., Cramer, H., & Brown, B. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441817 · Cited by 78 · see citing works
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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
- Authors
- Guerra, P. C., Meira, W., & Cardie, C.
- Affiliations
- Universidade Federal de Minas Gerais (BR)
- Cornell University (US)
- Venue
- Proceedings of the 7th ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2014-02-24
- Pages
- 443-452
- OpenAlex
- W1970464092
- Cited by
- 35
- References
- 3
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 2 · 2021: 5 · 2020: 5 · 2019: 4 · 2018: 3 · 2017: 3 · 2016: 3 · 2015: 5 · 2014: 3
- FWCI
- 2.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- sentiment analysis, social streams, concept drift, label acquisition, positive-negative asymmetry, social psychology
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