A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mining comparative sentences and relations
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Opinion extraction and summarization on the web
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Identifying types of claims in online customer reviews
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Aspect ranking: identifying important product aspects from online consumer reviews
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Product comparison using comparative relations
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Crowdsourcing recommendations from social sentiment
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Mining millions of reviews: a technique to rank products based on importance of reviews
Proceedings of the 13th International Conference on Electronic Commerce
Electronic Commerce Research
A probabilistic graphical model for brand reputation assessment in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Sentimental product recommendation
Proceedings of the 7th ACM conference on Recommender systems
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Increasingly large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. As the number of products being sold online increases, it is becoming increasingly difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, customer reviews, particularly the text describing the features, comparisons and experiences of using a particular product provide a rich source of information to compare products and make purchasing decisions. Online retailers like Amazon.com1 allow customers to add reviews of products they have purchased. These reviews have become a diverse and reliable source to aid other customers. Traditionally, many customers have used expert rankings which rate limited a number of products. Existing automated ranking mechanisms typically rank products based on their overall quality. However, a product usually has multiple product features, each of which plays a different role. Different customers may be interested in different features of a product, and their preferences may vary accordingly. In this paper, we present a feature-based product ranking technique that mines thousands of customer reviews. We first identify product features within a product category and analyze their frequencies and relative usage. For each feature, we identify subjective and comparative sentences in reviews. We then assign sentiment orientations to these sentences. By using the information obtained from customer reviews, we model the relationships among products by constructing a weighted and directed graph. We then mine this graph to determine the relative quality of products. Experiments on Digital Camera and Television reviews from real-world data on Amazon.com are presented to demonstrate the results of the proposed techniques.