The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
User-level sentiment analysis incorporating social networks
Open accessAbstract
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.
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Cites
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,720
Self-citationOpinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
Self-citationA sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
Pang, B., & Lee, L. (2004) · Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04 · doi:10.3115/1218955.1218990 · Cited by 3,301
Self-citationPang, 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
Self-citationFrom 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
Target-dependent Twitter Sentiment Classification
Jiang, L., Yu, M., Zhou, M., Liu, X., & Zhao, T. (2011) · Cited by 897
Self-citationRobust Sentiment Detection on Twitter from Biased and Noisy Data
Barbosa, L., & Feng, J. (2010) · Cited by 883
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
Davidov, D., Tsur, O., & Rappoport, A. (2010) · Cited by 700
Get out the vote: determining support or opposition from congressional floor-debate transcripts
Thomas, M., Pang, B., & Lee, L. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610122 · Cited by 483
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A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666
A survey of sentiment analysis in social media
Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019) · Knowledge and Information Systems · doi:10.1007/s10115-018-1236-4 · Cited by 577
Semantic Sentiment Analysis of Twitter
Saif, H., He, Y., & Alani, H. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35176-1_32 · Cited by 521
Exploiting social relations for sentiment analysis in microblogging
Hu, X., Tang, L., Tang, J., & Liu, H. (2013) · Proceedings of the sixth ACM international conference on Web search and data mining · doi:10.1145/2433396.2433465 · Cited by 384
Self-citationLearning Semantic Representations of Users and Products for Document Level Sentiment Classification
Tang, D., Qin, B., & Liu, T. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1098 · Cited by 365
Metadata
- Authors
- Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., & Li, P.
- Venue
- Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2011-08-21
- Pages
- 1397-1405
- OpenAlex
- W2143570397
- Cited by
- 419
- References
- 19
- FWCI
- 28.64
- Type
- conference paper
- Language
- en
- Open access
- green
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- Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., & Li, P. (2011). User-level sentiment analysis incorporating social networks. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2020408.2020614
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