SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS
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Abstract
We present two methods for determining the sentiment expressed by a movie review. The semantic orientation of a review can be positive, negative, or neutral. We examine the effect of valence shifters on classifying the reviews. We examine three types of valence shifters: negations, intensifiers, and diminishers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers and diminishers are used to increase and decrease, respectively, the degree to which a term is positive or negative. The first method classifies reviews based on the number of positive and negative terms they contain. We use the General Inquirer to identify positive and negative terms, as well as negation…
Citation impact
750
total citations
- FWCI
- 22.37
- Percentile
- 100%
- References
- 28
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Authors
2Topics & keywords
Topics
Keywords
- Bigram
- Valence (chemistry)
- Negation
- Natural language processing
- Computer science
- Artificial intelligence
- Term (time)
- Sentiment analysis
UN Sustainable Development Goals
- Quality Education
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