Lexicon-based methods for sentiment analysis

Authors:
Maite Taboada;Julian Brooke;Milan Tofiloski;Kimberly Voll;Manfred Stede
Affiliations:
Simon Fraser University;University of Toronto;Simon Fraser University;University of British Columbia;University of Potsdam
Venue:
Computational Linguistics
Year:
2011

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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.