The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Using appraisal groups for sentiment analysis
Abstract
Little work to date in sentiment analysis (classifying texts by `positive' or `negative' orientation) has attempted to use fine-grained semantic distinctions in features used for classification. We present a new method for sentiment classification based on extracting and analyzing appraisal groups such as ``very good'' or ``not terribly funny''. An appraisal group is represented as a set of attribute values in several task-independent semantic taxonomies, based on Appraisal Theory. Semi-automated methods were used to build a lexicon of appraising adjectives and their modifiers. We classify movie reviews using features based upon these taxonomies combined with standard ``bag-of-words'' features, and report state-of-the-art accuracy of 90.2%. In addition, we find that some types of appraisal appear to be more significant for sentiment classification than others.
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Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
Sentiment analysis using product review data
Fang, X., & Zhan, J. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0015-2 · Cited by 788
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
ARSA: a sentiment-aware model for predicting sales performance using blogs
Liu, Y., Huang, X., An, A., & Yu, X. (2007) · Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1277741.1277845 · Cited by 306
FinBERT: Financial Sentiment Analysis with Pre-trained Language Models
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Metadata
- Authors
- Whitelaw, C., Garg, N., & Argamon, S.
- Venue
- Proceedings of the 14th ACM international conference on Information and knowledge management
- Publisher
- ACM
- Published
- 2005-10-31
- Pages
- 625-631
- OpenAlex
- W2085582472
- Cited by
- 561
- References
- 25
- FWCI
- 9.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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