Discovering generalized association rules from Twitter
Intelligent Data Analysis
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In this paper, we have proposed a method to identify and track the drifted topics in the background of the social media through exploring the heated comments published, discussed, and voted by the participants of the social media. Based on this approach, we have further developed a way to optimize the recommendation of the relevant news to the readers of certain news by using the keywords generated from both news and comments. The challenge lies in how to select the keywords that are related with the drifted topics according to the users’ preference. In our work, we have utilized the number of votes received by a reader as an implicit feedback from the social media users in determining the quality of the comment. Then the keywords extracted from the comments are ranked based on both the quantity and the quality of the comments they appears in. Finally top-ranked keywords are selected and merged with the keywords representative of the original topics to retrieve the relevant news. Our experiment on news and comments from social media shows this approach is quite effective and promising.