The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Sentiment Knowledge Discovery in Twitter Streaming Data
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Citations
Cites
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Self-citationAuthoritative sources in a hyperlinked environment
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 8,959
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,720
Opinion mining and sentiment analysis
Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022 · cited 1×
“[5] show that popular users who have high indegree are not necessarily influential in terms of retweets or mentions, and that influence is gained through concerted effort such as limiting tweets to a single topic.”
BackgroundTwitter as a Corpus for Sentiment Analysis and Opinion Mining
Pak, A., & Paroubek, P. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/47akcj8foo4d · Cited by 2,704
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
Mining high-speed data streams
Domingos, P., & Hulten, G. (2000) · Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/347090.347107 · Cited by 2,185
Cited by
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
Self-citationEnsemble learning for data stream analysis: A survey
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.004 · Cited by 1,050
(2011) · Cited by 724
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
Ghiassi, M., Skinner, J., & Zimbra, D. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.057 · Cited by 485
Metadata
- Authors
- Bifet, A., & Frank, E.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2010
- Pages
- 1-15
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2145139979
- Cited by
- 573
- References
- 25
- FWCI
- 22.09
- Type
- conference paper
- Language
- en
- Open access
- green
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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.
