Shaming tweets detection on Twitter using Machine learning Algorithms

Twitter has essential and often unpleasant consequences in everyday life. Users have turned major social networking sites into a platform for disseminating much unnecessary and undesired material. Twitter has become one of the best and most popular little blogging services for sharing random thoughts. The majority of the participants who make comments on a specific occurrence are inclined to disgrace the victim. In this paper, to identify the shameful tweets or comments on twitter are done. Specifically, after identifying shameful tweets, it categorized into five categories: ill-treat, social comparison, Bad judgment, blasphemy, and unpleasant jokes, with each shaming tweet falling into one of these categories. After categorization the shaming user automatically blocked after giving the one message to that shamer. To detect these tweets, the system recommends utilizing machine learning classifiers like Random Forest, Naive Bayes, KNN. The classifier analysis aids in determining the accuracy of each for spotting shaming tweets. These classifiers are for better analysis of tweets.