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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
livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface
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
paper also provides a search feature to searching the highest similarity of historical information, using text-mining and clustering methods. This makes it easier for users to learning historical event. We compare result of our idea into several device and several keyword to searching history. The experimental result show
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.
difficulty due to the large size of the list of words in a thesaurus. In this paper, we present a new method for solving the problem of text categorization over a corpus of newspaper articles where the annotation must be composed of thesaurus elements. The method consists of applying lemmatization, obtaining keywords and named
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