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number of keywords for the searches of the events, especially in relation with analytics and searches on SNs for the reflectance of those events. A special attention is given to synonyms.
of that unrest on Phuket's tourism environment. It is proposed that this analysis can provide measurable insights through summarization, keyword analysis and clustering. We measure sentiment using a binary choice keyword algorithm. A multi-knowledge based approach is proposed using, Self-Organizing Maps along with
empirical rules, then, burst detection algorithm is adopted to discover peak interval of all phases, finally, we use a summarization technique TextRank to extract keywords from contents to summarize the topics in each phase. In addition, we perform experiments on two real-world datasets collected from different social media
Twitter). In our previous work, we developed a method for identifying local temporal burstiness to detect local hot keywords considering the users' location. The previous method is based on Kleinberg's temporal burst detection algorithm, which presupposes that the rate of posting remains constant. However, this leads to a
the public users (voters), collected by means of crawling during the course of election. In order to gain useful insights about the US elections, we scored the sentiments for each tweet using different metrics and performed a time series analysis for candidates and different topics (identified by specific keywords). In
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