2011 UserLevelSentimentAnalysisIncor

• (Tan et al., 2011) ⇒ Chenhao Tan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li. (2011). “User-level Sentiment Analysis Incorporating Social Networks.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020614

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+User-level+Sentiment+Analysis+Incorporating+Social+Networks
• http://dl.acm.org/citation.cfm?id=2020408.2020614&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; data mining; experimentation; miscellaneous; opinion mining; sentiment analysis; social networks; twitter

Abstract

We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower / followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 UserLevelSentimentAnalysisIncor	Lillian Lee Jie Tang Chenhao Tan Long Jiang Ming Zhou Ping Li			User-level Sentiment Analysis Incorporating Social Networks				10.1145/2020408.2020614		2011