An Efficient Tweeter Sentiment Analysis Sfcetr: Selective Feature Based Case Content Extraction Using Maximum Entropy Classifier To Rank The Tweets - Kavitha, P; Prabakaran, M (International Journal of Computer Sciences and Engineering, 2018)
Real world analysis the data based on the realistic approach to deal any objectives in online environment. Specifically social remedies have various approach of projective comments to share the information about products, innovative technologies etc. in this thing tweeter is a main platform to provide communication mammon the sharable users. In this provision most used by the people have the onions to make sentimental key terms to notify the originality. The sentiments about specific data be pointed by the comments in content format with sort text opinions. The opinions are extracted from the comments statement to analyst the tweeter data. By the fact of analyzing tweets have the hidden sentimental approach the problem arise due to right choice of sentimental extraction to classification is difficult. To overcome the problematic issue to propose a selective feature based case content extraction using maximum entropy classifier (SFCETR) to rank the tweets. This initially preprocess the tweets data the content reason from the comments statement. to aim the case reasons of relational features observed from the tweet contents are key term as contents .the comment case sentimental relation keyword terms are extracted synonmically to classify the data base on the reference key variable . Finally the rank case resultant categorize the sentimental case reasoning onion s about the predicative approach are classifies as class. This improves the tweets case opinions extraction are carried with the high performance sentimental research.