Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Sentiment Mining in WebFountain
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Text mining for product attribute extraction
ACM SIGKDD Explorations Newsletter
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
| Hi-index | 0.00 |
Customer reviews contain opinions of the customers who purchased products and expressed opinions concerning their satisfactions and criticisms. Due to vast availability of product reviews in the web, it is extremely time-consuming and at times confusing for a new customer to manually analyze the reviews prior to buying a product. Reviews generally involve the presence of product feature specific factual information along with the opinion sentences depicting the pros and cons of a bought product. The unstructured format of the text reviews from most of the web review sources necessitates the automatic identification of opinion sentences from the customer reviews, and also the identification of explicitly visible and implicitly present product features associated with the opinion sentences. In this paper, a process has been described where typed dependency relations such as open clausal complements or adjectival complements have been utilized to identify opinion sentences specific to product features. The typed dependency relations in the identified opinion sentences are then used to associate a product feature to an opinion sentence with the help of the product feature associated frequent words extracted from a previously managed customer review corpus.