DocumentCode :
3092942
Title :
Improving sentiment analysis with Part-of-Speech weighting
Author :
Nicholls, Chris ; Song, Fei
Author_Institution :
Dept. of Comput. & Inf. Sci., Univ. of Guelph, Guelph, ON, Canada
Volume :
3
fYear :
2009
fDate :
12-15 July 2009
Firstpage :
1592
Lastpage :
1597
Abstract :
Sentiment analysis is concerned with classifying the opinions in a piece of text. We present a term weighting scheme which takes into account part-of-speech categories to improve machine learning-based classification of sentiment in product reviews. We experimentally find optimal strengths for each part-of-speech category and show that using this weighting method improves overall sentiment classification.
Keywords :
learning (artificial intelligence); text analysis; machine learning-based classification; part-of-speech weighting; sentiment analysis; sentiment classification; term weighting scheme; Cybernetics; Electronic mail; Entropy; Information analysis; Information science; Internet; Labeling; Machine learning; Tagging; Text categorization; Feature Selection; Feature Weighting; Part-of-Speech Tagging; Sentiment Analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
Type :
conf
DOI :
10.1109/ICMLC.2009.5212278
Filename :
5212278
Link To Document :
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