Ontology-supported polarity mining
Journal of the American Society for Information Science and Technology
Novel relationship discovery using opinions mined from the web
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Graph ranking for sentiment transfer
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
CCRM: an effective algorithm for mining commodity information from threaded Chinese customer reviews
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
The relation between author mood and affect to sentiment in text and text genre
Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval
Between bags and trees – constructional patterns in text used for attitude identification
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine on-line 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 the Movie Reviews data set 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.