Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
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
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd 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
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
An empirical approach for opinion detection using significant sentences
AMT'10 Proceedings of the 6th international conference on Active media technology
Sentiment dictionary for effective detection of web users' opinion
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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In this paper we present an approach to identify opinion of web users from opinionated texts and to classify web users opinion into positive or negative. It is found that a few opinionated texts even though opinionated yields values that are classified neither as positive nor as negative by opinion detection algorithm. When an opinionated text is subjected to opinion detection algorithm, it yields a value that is lesser or greater or equal to the threshold. If it is less than the threshold, it is classified as negative opinion. If it is greater than the threshold, it is classified as positive opinion. If it is equal to the threshold, it is considered as neutral opinion. Different approaches can be considered to obtain opinion from these computed neutral texts so as to classify texts efficiently as positive or negative. We propose the use of pattern and keyword based approach for detection and classification of users opinion from opinionated texts. Our approach is effective in detecting opinion and reducing in-correct classifications. It is found to better than the other implemented methods on different data sets.