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This paper proposes a mutual detection mechanism between spam blogs and keywords for filtering spam blogs from updated blog data. Spam blogs are problematic in extracting useful marketing information from the blogosphere; they often appear to be rich sources of information based on individual opinion and social
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
using keywords graph to contribute special techniques for exploring those groups and the relationships among them. Interactions between users and the created keywords graph are also provided. Compared to other applications on blog visualization, our approach utilized the ontology knowledge to analysis and automatically
problem. In this paper, we propose a novel vlog management model which is comprised of automatic vlog annotation and user-oriented vlog search. For vlog annotation, we extract informative keywords from both the target vlog itself and relevant external resources; besides semantic annotation, we perform sentiment analysis on
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