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In this paper we present the work done on social media analysis to predict civil unrest using keyword filtering. The information given on the social media is delivered to every person within the fraction of seconds. This rapid circulation of information and the people opinions through social platform affects or create
context to disambiguate. In this paper we address the filtering task of determining, out of a set of tweets that contain a company name, which ones do refer to the company. Our approach relies on the identification of filter keywords: those whose presence in a tweet reliably confirm (positive keywords) or discard (negative
. In any politically motivated posting there are some dominant keywords. At first, we have prepared a dictionary consisting of unique words collected from political or nonpolitical posts or comments and then trained using Naïve Bayes algorithm based on probability theory. To identify the sentiment expressed in a
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