Biterm language models for document retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Communications of the ACM - The Blogosphere
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Incorporating term dependency in the dfr framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking opinionated blog posts using OpinionFinder
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Limits of opinion-finding baseline systems
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
A case study of distributed information retrieval architectures to index one terabyte of text
Information Processing and Management: an International Journal
Automatic construction of an opinion-term vocabulary for ad hoc retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Topic-Related Polarity Classification of Blog Sentences
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
ACM SIGIR Forum
Effective and efficient polarity estimation in blogs based on sentence-level evidence
Proceedings of the 20th ACM international conference on Information and knowledge management
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
A new generative opinion retrieval model integrating multiple ranking factors
Journal of Intelligent Information Systems
Score transformation in linear combination for multi-criteria relevance ranking
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media
ACM Transactions on Intelligent Systems and Technology (TIST)
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
Effective sentence retrieval based on query-independent evidence
Information Processing and Management: an International Journal
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
Natural language opinion search on blogs
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Hi-index | 0.00 |
Opinion finding is a challenging retrieval task, where it has been shown that it is especially difficult to improve over a strongly performing topic-relevance baseline. In this paper, we propose a novel approach for opinion finding, which takes into account the proximity of query terms to subjective sentences in a document. We adapt two state-of-the-art opinion detection techniques to identify subjective sentences from the retrieved documents. Our first technique uses the OpinionFinder toolkit to classify the subjectiveness of sentences in a document. Our second technique uses an automatically generated dictionary of subjective terms derived from the document collection itself to identify the most subjective sentences in a document. We extend the Divergence From Randomness (DFR) proximity model to integrate the proximity of query terms to the subjective sentences identified by either of the proposed techniques. We evaluate these techniques on five different strong baselines across two different query datasets from the TREC Blog track. We show that we can significantly improve over the baselines and that, in several settings, our proposed techniques can at least match the top performing systems at the TREC Blog track.