The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Statistical Model for Relevance Feedback in Information Retrieval
Journal of the ACM (JACM)
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Adaptive Assistants for Customized E-Shopping
IEEE Intelligent Systems
A personalized and integrative comparison-shopping engine and its applications
Decision Support Systems - Special issue: Agents and e-commerce business models
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
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
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
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
Computer
Cognitive fit in requirements modeling: a study of object and process methodologies
Journal of Management Information Systems - Special section: Strategic and competitive information systems
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Pricing and Product Design: Intermediary Strategies in an Electronic Market
International Journal of Electronic Commerce
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Collecting evaluative expressions for opinion extraction
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Identifying implicit relationships between social media users to support social commerce
Proceedings of the 14th Annual International Conference on Electronic Commerce
Hi-index | 0.00 |
Due to the popularity of online retail stores, consumers are not only shopping and comparing consumer products on the Web but also providing their consumer reviews on the Internet platform. The Web has become the largest repository of consumer reviews. Consumer reviews are beneficial to consumers, merchants, and manufacturers. Consumers may read the comments of other consumers and decide whether the product is good in the specific product features that they are interested in. For merchants or product manufacturers, consumer reviews help them understand general responses of customers on their products for product or marketing campaign improvement. In addition, consumer reviews can enable merchants better understand specific preferences of individual customers and facilitates effective marketing decisions. However, the large volume of consumer reviews makes it impossible for any individual consumer, merchant, or manufacturer to extract important knowledge efficiently. In this study, we concentrate on opinion sentence identification of focused sentiment analysis and propose a collaborative-filtering-based opinion sentence identification (CF-OSI) technique. The proposed CF-OSI technique considers opinion sentence identification as the sentence retrieval problem. In addition, a collaborative-filtering-based query expansion approach is incorporated into the CF-OSI technique to address possible effectiveness degradation caused by short user queries (i.e., limited number of query terms in query queries). Experiments have been conducted to empirically evaluate the effectiveness of our proposed technique. Our evaluation results show that the performance of our proposed CF-OSI technique is promising.