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their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as
detect user sentiments. The keyword-based approaches for identifying such themes fail to give satisfactory level of accuracy. Here, we address the above problems using statistical text-mining of blog entries. The crux of the analysis lies in mining quantitative information from textual entries. Once the relevant blog
. Second, sentiment is used as features to detect bursts in microblog streams online. At last, we exploit regular expressions to extract hashtags in bursty periods and segment hashtags into keywords. By using mutual information and frequent patterns, we fetch words relevant to hashtags as keywords to form events. This
track user‐generated messages that contain links to New York Times online articles and we label users according to the topic of the links they share, their geographic location, and their self‐descriptive keywords. When users are clustered based on who follows whom in Twitter, we find social groups separate by whether they
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