Affective computing
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Whose thumb is it anyway?: classifying author personality from weblog text
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparing corpora using frequency profiling
CompareCorpora '00 Proceedings of the Workshop on Comparing Corpora
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
Generating contextualized sentiment lexica based on latent topics and user ratings
Proceedings of the 24th ACM Conference on Hypertext and Social Media
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This paper describes preliminary work on feature selection for classification of review text by both sentiment rating and topic. The premise stems from the notion that one size does not fit all; that feature sets for sentiment analysis should be tailored to the topic of a text. Thus it naturally follows that for this to be effective it is also necessary to first determine the topic of a text. Following successful work on classification of texts by author demographics, a corpus of review texts labelled with attributed rating, topic area, and user demographics has been compiled. This collection was divided for this work into different topic groups in order to automatically classify between both text topic and subjective rating. By using a single supervised statistical approach to feature selection, it is shown that improvements can be made to classification accuracy using topic tuned features sets over more generic features.