Lexicon-Based Methods for Sentiment Analysis

by Maite Taboada , Milan Tofiloski , Julian Brooke , Kimberly Voll , Manfred Stede

Citations:

12 - 1 self

Datum	Value	Source
TITLE	Lexicon-Based Methods for Sentiment Analysis	SVM HeaderParse 0.2
AUTHOR NAME	Maite Taboada	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Toronto	SVM HeaderParse 0.2
AUTHOR NAME	Milan Tofiloski	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Toronto	SVM HeaderParse 0.2
AUTHOR NAME	Julian Brooke	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Toronto	SVM HeaderParse 0.2
AUTHOR NAME	Kimberly Voll	SVM HeaderParse 0.2
AUTHOR AFFIL	University of British Columbia	SVM HeaderParse 0.2
AUTHOR NAME	Manfred Stede	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Potsdam	SVM HeaderParse 0.2
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. 1.	SVM HeaderParse 0.2
CITATIONS	124 found	ParsCit 1.0