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interest for people so in this paper an easy approach of gathering and analyzing data through keyword based search in social networks is examined using NodeXL and data is gathered from twitter in which political trends have been analyzed. As a result it will be analyzed that, what people are focusing most in politics.
monthly automobile sales using sentiment and topical keyword frequencies related to the target brand over time on social media. Our predictive model illustrates how different time scale-based predictors derived from sentiment and topical keyword frequencies can improve the prediction of the future sales.
With Web 2.0 advances, social computing has become an emerging research field in the past decade. This article analyzes the characteristics of social computing research from both static and dynamic perspectives. First, the authors present the overlapping relationships of content represented by keywords as of 2011
aimed to analyze the studies according to variable frequency, work group, data collection tool, research methods and distribution of studies among countries. This study aims to evaluate the results of the publications that are found in the ProQuest Dissertations & Theses Full Text with the keyword “classroom
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