The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Joint sentiment/topic model for sentiment analysis
Abstract
Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.
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Cites
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Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
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
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Probabilistic Latent Semantic Indexing
Hofmann, T. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130370 · Cited by 3,988
Probabilistic latent semantic indexing
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Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
Peter, P., & Turney (2002) · Cited by 3,650
A 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
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
Turney, P. D. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073153 · Cited by 2,352
(2007) · Cited by 2,018
Cited by
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02145-9 · Cited by 3,281
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,260
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
Aspect and sentiment unification model for online review analysis
Jo, Y., & Oh, A. H. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935932 · Cited by 767
Document-level sentiment classification: An empirical comparison between SVM and ANN
Moraes, R., Valiati, J. F., & Gavião Neto, W. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.07.059 · Cited by 733
Metadata
- Authors
- Lin, C., & He, Y.
- Venue
- Proceedings of the 18th ACM conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2009-11-02
- Pages
- 375-384
- OpenAlex
- W2108420397
- Cited by
- 961
- References
- 27
- FWCI
- 21.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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