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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
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
We have been developing a system, called Tour Miner, which mines tour plans from SNS. It consists of two functions: mining and smelting. The mining function searches SNS for a given keyword and discovers travel records related to the keyword. The smelting function combines the travel records and extracts tour plans
present a more informative result compared to conventional search engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword search on twitter and Google. We have evaluated our method on collected social network corpus related to specific
, social networks such as Facebook, and data from news stations. Such geo-textual data allows to immediately detect and react to new and emerging trends. A trend is a set of keywords associated with a time interval where the frequency of these keywords is increased significantly. In this paper, we investigate the
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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