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propose Term-Frequency and Inverse Document Frequency (TF-IDF) method to rank keywords of top twenty most followed Instagram users based on image captions of Instagram. The objective of this research is to automatically know the main idea of Instagram users based on 50 recent image captions posted. In our experiments, TF-IDF
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
information overload. Analyzing social audience who are interested in a company of social media is very difficult and so many text mining methods e.g. fuzzy keyword match method, Twitter LDA method and Machine learning approaches are used for solving this problem. Using the tweets of the account owner to segment followers and
posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in
trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content.
Twitter is a user-friendly social network which deserves its real-time nature. With the help of an algorithm, the investigation can be made with regard to some of the real-time events such as earthquake. The target event is assumed and classified based on the keywords, number of words and their context. The
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