Sentiment analysis: capturing favorability using natural language processing Export

@inproceedings{citeulike:3738298, 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.}, address = {New York, NY, USA}, author = {Nasukawa, Tetsuya and Yi, Jeonghee}, booktitle = {K-CAP '03: Proceedings of the 2nd international conference on Knowledge capture}, citeulike-article-id = {3738298}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=945658}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/945645.945658}, doi = {10.1145/945645.945658}, isbn = {1-58113-583-1}, keywords = {sentiment}, location = {Sanibel Island, FL, USA}, pages = {70--77}, posted-at = {2008-12-03 01:52:11}, priority = {3}, publisher = {ACM}, title = {Sentiment analysis: capturing favorability using natural language processing}, url = {http://dx.doi.org/10.1145/945645.945658}, year = {2003} }

TY - CONF ID - citeulike:3738298 L3 - citeulike-article-id:3738298 N2 - 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. CY - Sanibel Island, FL, USA SN - 1-58113-583-1 AD - New York, NY, USA EP - 77 TI - Sentiment analysis: capturing favorability using natural language processing PB - ACM T2 - K-CAP '03: Proceedings of the 2nd international conference on Knowledge capture SP - 70 KW - sentiment AU - Nasukawa, Tetsuya AU - Yi, Jeonghee PY - 2003/// UR - http://dx.doi.org/10.1145/945645.945658 ER -