Sentiment analysis of blogs by combining lexical knowledge with text classification Export

@inproceedings{citeulike:5413128, abstract = {The explosion of user-generated content on the Web has led to new opportunities and significant challenges for companies, that are increasingly concerned about monitoring the discussion around their products. Tracking such discussion on weblogs, provides useful insight on how to improve products or market them more effectively. An important component of such analysis is to characterize the sentiment expressed in blogs about specific brands and products. Sentiment Analysis focuses on this task of automatically identifying whether a piece of text expresses a positive or negative opinion about the subject matter. Most previous work in this area uses prior lexical knowledge in terms of the sentiment-polarity of words. In contrast, some recent approaches treat the task as a text classification problem, where they learn to classify sentiment based only on labeled training data. In this paper, we present a unified framework in which one can use background lexical information in terms of word-class associations, and refine this information for specific domains using any available training examples. Empirical results on diverse domains show that our approach performs better than using background knowledge or training data in isolation, as well as alternative approaches to using lexical knowledge with text classification.}, address = {New York, NY, USA}, author = {Melville, Prem and Gryc, Wojciech and Lawrence, Richard D.}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {5413128}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1557019.1557156}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1557019.1557156}, doi = {10.1145/1557019.1557156}, isbn = {978-1-60558-495-9}, keywords = {analysis, blogs, domain, knowledge, ml, multinomials, pooling, sentiment}, location = {Paris, France}, pages = {1275--1284}, posted-at = {2009-10-21 09:47:24}, priority = {0}, publisher = {ACM}, title = {Sentiment analysis of blogs by combining lexical knowledge with text classification}, url = {http://dx.doi.org/10.1145/1557019.1557156}, year = {2009} }

TY - CONF ID - citeulike:5413128 L3 - citeulike-article-id:5413128 N2 - The explosion of user-generated content on the Web has led to new opportunities and significant challenges for companies, that are increasingly concerned about monitoring the discussion around their products. Tracking such discussion on weblogs, provides useful insight on how to improve products or market them more effectively. An important component of such analysis is to characterize the sentiment expressed in blogs about specific brands and products. Sentiment Analysis focuses on this task of automatically identifying whether a piece of text expresses a positive or negative opinion about the subject matter. Most previous work in this area uses prior lexical knowledge in terms of the sentiment-polarity of words. In contrast, some recent approaches treat the task as a text classification problem, where they learn to classify sentiment based only on labeled training data. In this paper, we present a unified framework in which one can use background lexical information in terms of word-class associations, and refine this information for specific domains using any available training examples. Empirical results on diverse domains show that our approach performs better than using background knowledge or training data in isolation, as well as alternative approaches to using lexical knowledge with text classification. CY - Paris, France SN - 978-1-60558-495-9 AD - New York, NY, USA EP - 1284 TI - Sentiment analysis of blogs by combining lexical knowledge with text classification PB - ACM T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 1275 KW - analysis KW - blogs KW - domain KW - knowledge KW - ml KW - multinomials KW - pooling KW - sentiment AU - Melville, Prem AU - Gryc, Wojciech AU - Lawrence, Richard PY - 2009/// UR - http://dx.doi.org/10.1145/1557019.1557156 ER -