A Comprehensive Examination of Toxic Tweet Classification on Twitter

In recent times, daily interactions have been taking place largely on online platforms, with forms of interactions such as emailsandtextmessages.However,an important problem lies in the understanding of these messages. These textual interchanges can be interpreted in multiple different ways by different people which can cause significant misunderstandings and negative perceptions. The aim of this project is to address this challenge and overcome it by developing a system that is capable of identifying the negativity and toxicity in the tone of the messages. Through rigorous analysis the model aims to identify parts or sequences of a message that might appear as offensive or negative, giving the user constructive feedback on how they can better improve their messages. This helps users to write messages that are not only clearer but also easily received in the intended manner by their audience, thereby increasing the effectiveness of the communication process and reducing misunderstandings. The main goal of this paper is to contribute to the development of an inclusive and understanding online community by using techniques of Natural Language Processing and Machine Learning to solve the complexities of human communication.