Diversifying search results of controversial queries
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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. © 2011 ACM.