Lexicon-Based Methods for Sentiment Analysis
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Abstract
We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.
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The record
- Venue
- Computational Linguistics
- Topic
- Sentiment Analysis and Opinion Mining
- Field
- Computer Science
- Canadian institutions
- University of British ColumbiaUniversity of TorontoSimon Fraser University
- Funders
- Social Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
- Keywords
- Computer scienceSentiment analysisLexiconPolarity (international relations)Natural language processingArtificial intelligenceNegationConsistency (knowledge bases)Orientation (vector space)Word (group theory)Process (computing)Reliability (semiconductor)LinguisticsMathematics
- Has abstract in OpenAlex
- yes