An algebraic approach to linguistic hedges in Zadeh's fuzzy logic
Fuzzy Sets and Systems - Data bases and approximate reasoning
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
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
Using appraisal groups for sentiment analysis
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
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Lexicon-based methods for sentiment analysis
Computational Linguistics
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
| Hi-index | 0.00 |
Discerning a consensus opinion about a product or service is difficult due to the many opinions on the web. To overcome this problem, sentiment classification has been applied as an important approach for evaluation in sentiment mining. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques such as unsupervised and machine learning methods. This paper proposes an unsupervised method for classifying the polarity of reviews using a combination of methods including PMI, SentiWordNet and adjusting the phrase score in the case of modification. The experiment results show that the proposed system achieves accuracy ranging from 69.36% for movie reviews to 80.16% for automotive reviews.