Classifying Texts of Twitter Data Using a Modified Fuzzy Logic Method

Social media are a modern web-based application for communication between humans. People share their interests and activities with these Applications. Twitter is a social media site, where people communicate through tweets. People publish their tweets on their profile and send their followers to express their thoughts and opinions about events in this world. In this research, a modified fuzzy logic method to disband text classification problem. The Inputs for this classification system are a set of features extracted from a tweet and the output of this system is a decision of classification for a tweet, which is a degree of correlation for each tweet to an appointed event where the degree of relevance to the desired event if it irrelevant or relevant. The results compared with the keyword search method and the previous fuzzy logic based method based on terms of correction rate and incremental rate. In the incremental rate, the proposed system is able to extract tweets more than a previous fuzzy logic based method, where in dataset 1 the number of the tweets that extracted by the proposed system is 154tweets but the number of the tweets that extracted by the other one are 98 and 141. The correction rate of the proposed system is (98.7) but the correction rates of these methods are (97.9) and (95.7).