Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for 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
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Exploitation in affect detection in open-ended improvisational text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis
ECIR'07 Proceedings of the 29th European conference on IR research
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We are interested in finding how people feel about certain topics. This could be considered as a task of classifying the sentiment: sentiment could be positive, negative or neutral. In this paper, we examine the problem of automatic sentiment analysis at sentence level. We observe that sentence structure has a fair contribution towards sentiment determination, and conjunctions play a major role in defining the sentence structure. Our assumption is that in presence of conjunctions, not all phrases have equal contribution towards overall sentiment. We compile a set of conjunction rules to determine relevant phrases for sentiment analysis. Our approach is a representation of the idea to use linguistic resources at phrase level for the analysis at sentence level. We incorporate our approach with support vector machines to conclude that linguistic analysis plays a significant role in sentiment determination. Finally, we verify our results on movie, car and book reviews.