Social Media Text Data Classification using Enhanced TF_IDF based Feature Classification using Naive Bayesian Classifier - M. Ayyappa Chakravarthi, Dr. M. Thillaikarasi, Dr. Bhanu Prakash Battula (International Journal of Advanced Science and Technology, 2020)
From last few years the amount of data generated in growing exponentially. Due to the advancement in technology. All most every data in present world warehoused in the form of electronic documentsMore importantly social media data is growing exponentially. Text analysis is a core part of present researchers. It includes sentiment analysis, event and topic detection, geolocation identification, spam detection, review analysis and summarization. Mining text data is the process of extracting the text features from text data. Text classification is the process of classifying text documents into fixed number of predefined classes. The application of text classification includes spam filtering, email routing, sentiment analysis, language identification etc. this paper address the text classification of social media data by applying novel feature extraction and feature selection mechanisms.