Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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
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
Improving personalized web search using result diversification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Probabilistic models of ranking novel documents for faceted topic retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Language-model-based pro/con classification of political text
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Selectively diversifying web search results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Clustering product features for opinion mining
Proceedings of the fourth ACM international conference on Web search and data mining
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Approximation algorithms for maximum dispersion
Operations Research Letters
MOUNA: mining opinions to unveil neglected arguments
Proceedings of the 21st 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
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Diversifying search results of queries seeking for different view points about controversial topics is key to improving satisfaction of users. The challenge for finding different opinions is how to maximize the number of discussed arguments without being biased against specific sentiments. This paper addresses the issue by first introducing a new model that represents the patterns occurring in documents about controversial topics. Second, proposing an opinion diversification model that uses (1) relevance of documents, (2) semantic diversification to capture different arguments and (3) sentiment diversification to identify positive, negative and neutral sentiments about the query topic. We have conducted our experiments using queries on various controversial topics and applied our diversification model on the set of documents returned by Google search engine. The results show that our model outperforms the native ranking of Web pages about controversial topics by a significant margin.