Fuzzy logic and Genetic Algorithm based Text Classification Twitter

Social media are a modern web-based application for communication and interaction between humans through audio messages, written messages, and video messages. These devices build and activate living communities around the world. People share their interests and activities with these Applications. Twitter is a social media site, where people communicate through tweets. A service that enables users to communicate in touch through the exchange of frequent tweets and quick. People publish their tweets on their profile and send their followers to express their thoughts and opinions about events in this world. It is crucial to study and categorize these tweets. This work is based on fuzzy logic and genetic algorithm to solve the problem of text classification based on the relevance degree. 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 relevance for each tweet to an appointed event where the degree of relevance to the desired event iftweet irrelevant or relevant. The results are compared with a method of keyword search and fuzzy logic, which is based on the method incremental rate and correction rate. In the incremental rate, the proposed system is able to extract tweets more than this method, wherein dataset 1, the number of the tweets that are extracted by the proposed system is 160 tweets, but the number of the tweets that extracted by the other one are 98 and141. The correction rate of the proposed system is (98.75), but the correction rates of this method are (97.9) and (95.7).