The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques
Abstract
We present sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general Webpages and news articles.
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Cites
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405
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
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
Peter, P., & Turney (2002) · Cited by 3,650
Accurate methods for the statistics of surprise and coincidence
Dunning, T. E. (1993) · doi:10.5555/972450.972454 · Cited by 2,647
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
Thumbs up? Sentiment Classification using Machine Learning Techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205070 · Cited by 2,191
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
Dave, K., Lawrence, S., & Pennock, D. M. (2003) · Proceedings of the twelfth international conference on World Wide Web - WWW '03 · doi:10.1145/775152.775226 · Cited by 1,916
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
Turney, P. D. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0212032 · Cited by 1,575
Predicting the semantic orientation of adjectives
Hatzivassiloglou, V., & McKeown, K. R. (1997) · Proceedings of the 35th annual meeting on Association for Computational Linguistics - · doi:10.3115/976909.979640 · Cited by 1,439
Cited by
Natural language processing: state of the art, current trends and challenges
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13428-4 · Cited by 1,823
Sentiment analysis: A combined approach
Prabowo, R., & Thelwall, M. (2009) · Journal of Informetrics · doi:10.1016/j.joi.2009.01.003 · Cited by 803
Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2009) · Computational Linguistics · doi:10.1162/coli.08-012-r1-06-90 · Cited by 722
Survey on mining subjective data on the web
Tsytsarau, M., & Palpanas, T. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0238-6 · Cited by 443
AL-Smadi, M., Hammad, M. M., Al-Zboon, S. A., AL-Tawalbeh, S., & Cambria, E. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107540 · Cited by 62
Metadata
- Authors
- Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W.
- Venue
- Third IEEE International Conference on Data Mining
- Publisher
- IEEE Comput. Soc
- Published
- 2004-03-30
- Pages
- 427-434
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2126854223
- Cited by
- 722
- References
- 22
- FWCI
- 10.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2004). Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. Third IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2003.1250949
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