REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
Opinion mining from noisy text data
Proceedings of the second workshop on Analytics for noisy unstructured text data
WaterCooler: exploring an organization through enterprise social media
Proceedings of the ACM 2009 international conference on Supporting group work
Matching reviews to objects using a language model
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
What do people ask their social networks, and why?: a survey study of status message q&a behavior
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The relationship between website quality, trust and price premiums at online auctions
Electronic Commerce Research
From federated to aggregated search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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The term online reputation addresses trust relationships amongst agents in dynamic open systems. These can appear as ratings, recommendations, referrals and feedback. Several reputation models and rating aggregation algorithms have been proposed. However, finding a trusted entity on the web is still an issue as all reputation systems work individually. The aim of this project is to introduce a global reputation system for electronic product reviews that aggregates people's opinions from different resources (e.g. e-commerce websites, and review) with the help federated search techniques and generate a high quality and trusted result. The first step is to choose a range of product review collections from e-commerce review systems (e.g. Amazon), online review sites (e.g. Epinions), social networks (e.g. Facebook), question and answering sites (e.g. Yahoo! Answers), and blog (e.g. My Nokia Blog) resources. By using a federated search approach the query (product name) will be broadcasted to the selected resources and the result will be a list of reputation data with various formats including star rating, text reviews, voting, video, and so on. The focus of this work is on review text data and star ratings. A number of challenges including comparison issues (e.g. scale of star ratings: five-star vs. ten-star), hierarchical reviews (e.g. comments about reviews), choice of resources (e.g. choosing relevant sources deepens upon query), display issue (e.g. easy for the user), generalization issue (e.g. apply it on other domains), synchronization problem (e.g. generate up-to-date results), and high quality and trusted reviews will be addressed. A sentiment analysis approach is subsequently used to extract high quality opinions and inform how to increase trust in the search result. The extracted opinions will be used to generate facets for the global reputation system.