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As an SNS, Twitter is popular because users can post their emotions as a short message easily. Emotional tweets may influence user relationships. In our previous study, we found that positive users construct mutual relationships in Twitter. Keyword matching with emotional word dictionaries was used to detect positive
temporal changes of brand-related keyword networks. Our analysis enables trends in brand awareness to be systematically traced and evaluated. This allows various other analyses, such as advantages and disadvantages of the brand, and a comparison with its competitors.
keywords from messages posted on social media which will be helpful in the identification of various communities, category of user and hidden pattern present in the social media. In this paper, we applied Probalistic approach to recognize the new keywords and assign the group accordingly. State-of-the-art studies performed
, irrelevant tweets were further segregated by means of a unigram dictionary containing education-oriented keywords. The Apriori algorithm was then applied to the dataset thus obtained resulting in characteristic markers or patterns of these institutes.
. 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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