Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Selectively diversifying web search results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Diversifying search results of controversial queries
Proceedings of the 20th ACM international conference on Information and knowledge management
Sentiment diversification with different biases
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Detecting controversy on the web
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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A query topic can be subjective involving a variety of opinions, judgments, arguments, and many other debatable aspects. Typically, search engines process queries independently from the nature of their topics using a relevance-based retrieval strategy. Hence, search results about subjective topics are often biased towards a specific view point or version. In this demo, we shall present MOUNA, a novel approach for opinion diversification. Given a query on a subjective topic, MOUNA ranks search results based on three scores: (1) relevance of documents, (2) semantic diversity to avoid redundancy and capture the different arguments used to discuss the query topic, and (3) sentiment diversity to cover a balanced set of documents having positive, negative, and neutral sentiments about the query topic. Moreover, MOUNA enhances the representation of search results with a summary of the different arguments and sentiments related to the query topic. Thus, the user can navigate through the results and explore the links between them. We provide an example scenario in this demonstration to illustrate the inadequacy of relevance-based techniques for searching subjective topics and highlight the innovative aspects of MOUNA. A video showing the demo can be found in http://www.youtube.com/user/mounakacimi/videos .