A Social Media Text Analysis Technique Using ML Algorithm

Data mining and ML approaches are used for analyzing the raw data. The analysis consequences has depends on applications requirements. use of also been become popular in social media data based NLP uncovering sentiments text post. Social two sides brighten dark. There a number creative ways to media, but there some users that accomplishing their toxic intensions using media. machine learning algorithms with helps us performing prediction, classification, categorization finding relationship among attributes. Therefore, this paper we have exploring difference technique by classification. In context publically available dataset from Kaggle collected model presented classify support vector (SVM) backpropagation neural network (BPN) classifiers. order extract features TF-IDF second scenario Part speech (POS) tagger. obtained results demonstrate performance BPN classifier is higher both scenarios feature Additionally simple subject classification providing more yield as compared POS features. Keywords—machine algorithm, supervised learning, data, features, performance.