This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.
%0 Conference Paper
%1 945658
%A Nasukawa, Tetsuya
%A Yi, Jeonghee
%B K-CAP '03: Proceedings of the 2nd international conference on Knowledge capture
%C New York, NY, USA
%D 2003
%I ACM
%K nlp sentiment_analysis text_mining
%P 70--77
%R http://doi.acm.org/10.1145/945645.945658
%T Sentiment analysis: capturing favorability using natural language processing
%U http://portal.acm.org/citation.cfm?doid=945645.945658#
%X This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.
%@ 1-58113-583-1
@inproceedings{945658,
abstract = {This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.},
added-at = {2008-03-18T19:11:59.000+0100},
address = {New York, NY, USA},
author = {Nasukawa, Tetsuya and Yi, Jeonghee},
biburl = {https://www.bibsonomy.org/bibtex/2d0b8bddd834bcf3a278c3c1c4f2224bf/hkorte},
booktitle = {K-CAP '03: Proceedings of the 2nd international conference on Knowledge capture},
description = {Sentiment analysis},
doi = {http://doi.acm.org/10.1145/945645.945658},
interhash = {f17664c2aec42af235d3a1fb382c0a81},
intrahash = {d0b8bddd834bcf3a278c3c1c4f2224bf},
isbn = {1-58113-583-1},
keywords = {nlp sentiment_analysis text_mining},
location = {Sanibel Island, FL, USA},
pages = {70--77},
publisher = {ACM},
timestamp = {2009-09-24T11:03:48.000+0200},
title = {Sentiment analysis: capturing favorability using natural language processing},
url = {http://portal.acm.org/citation.cfm?doid=945645.945658#},
year = 2003
}