Lexicon-Based Methods for Sentiment Analysis

• Published in: Computational linguistics - Association for Computational Linguistics Vol. 37; no. 2; pp. 267 - 307
• Main Authors: Taboada, Maite, Brooke, Julian, Tofiloski, Milan, Voll, Kimberly, Stede, Manfred
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.06.2011; MIT Press Journals, The; The MIT Press
• Subjects: Computational Linguistics; Dictionaries; Information processing; Lexical Semantics; Linguistics; Natural language processing; Psycholinguistics; Psychology; Sentiment analysis; Speech; Word Meaning
• ISSN: 0891-2017; 1530-9312; 1530-9312
• DOI: 10.1162/COLI_a_00049

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.