Completely-arbitrary passage retrieval in language modeling approach
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Applying completely-arbitrary passage for pseudo-relevance feedback in language modeling approach
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
ACM SIGIR Forum
Generating focused topic-specific sentiment lexicons
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A unified graph model for sentence-based opinion retrieval
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
Bootstrapping subjectivity detection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Find me opinion sources in blogosphere: a unified framework for opinionated blog feed retrieval
Proceedings of the fifth ACM international conference on Web search and data mining
An effective approach for topic-specific opinion summarization
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
Aggregation Methods for Proximity-Based Opinion Retrieval
ACM Transactions on Information Systems (TOIS)
Exploiting syntactic and semantic relationships between terms for opinion retrieval
Journal of the American Society for Information Science and Technology
| Hi-index | 0.00 |
Lexicon-based approaches have been widely used for opinion retrieval due to their simplicity. However, no previous work has focused on the domain-dependency problem in opinion lexicon construction. This paper proposes simple feedback-style learning for query-specific opinion lexicon using the set of top-retrieved documents in response to a query. The proposed learning starts from the initial domain-independent general lexicon and creates a query-specific lexicon by re-updating the opinion probability of the initial lexicon based on top-retrieved documents. Experimental results on recent TREC test sets show that the query-specific lexicon provides a significant improvement over previous approaches, especially in BLOG-06 topics.