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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.
In this paper we mine over 80 million twitter micro logs in order to explore whether data from this social media initiative can be used to identify sentiment about tourism and Thailand amid the unrest in that country during the early part of 2010 and further whether analysis of tweets can be used to discern the effect
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
Burstiness has been one of the most important criteria for extracting topics and events from documents posted on social media. Recently, researchers are focusing on extracting geolocal topics and events from such social documents because of the increasing number of geo-annotated documents (e.g., Geo-tagged tweets on
The main objective of this paper is to compare the sentiments that prevailed before and after the presidential elections, held in both US and France in the year 2012. To achieve this objective we extracted the content information from a social medium such as Twitter and used the tweets from electoral candidates and
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