Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts Export

@incollection{citeulike:2410610, abstract = {Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine online opinions from the Internet and learn customers' preferences for economic or marketing research, or for leveraging a strategic advantage. In this paper, we propose a two-stage Bayesian algorithm that is able to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. Experimental results on online movie reviews and online news show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several state-of-the-art machine learning methods. Our findings suggest that sentiments are captured by conditional dependence relations among words, rather than by keywords or high-frequency words.}, author = {Airoldi, Edoardo and Bai, Xue and Padman, Rema}, citeulike-article-id = {2410610}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11899402_11}, doi = {10.1007/11899402_11}, journal = {Advances in Web Mining and Web Usage Analysis}, keywords = {algorithm, annotation, bayesian, conceptual\_analysis, dependencies, extraction, sentiment, tagging}, pages = {167--187}, posted-at = {2008-04-27 17:46:40}, priority = {4}, title = {Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts}, url = {http://dx.doi.org/10.1007/11899402_11}, year = {2006} }

TY - CHAP ID - citeulike:2410610 L3 - citeulike-article-id:2410610 N2 - Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine online opinions from the Internet and learn customers’ preferences for economic or marketing research, or for leveraging a strategic advantage. In this paper, we propose a two-stage Bayesian algorithm that is able to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. Experimental results on online movie reviews and online news show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several state-of-the-art machine learning methods. Our findings suggest that sentiments are captured by conditional dependence relations among words, rather than by keywords or high-frequency words. JF - Advances in Web Mining and Web Usage Analysis EP - 187 TI - Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts SP - 167 KW - algorithm KW - annotation KW - bayesian KW - conceptual_analysis KW - dependencies KW - extraction KW - sentiment KW - tagging AU - Airoldi, Edoardo AU - Bai, Xue AU - Padman, Rema PY - 2006/// UR - http://dx.doi.org/10.1007/11899402_11 ER -