Classification of Code-Mixed Tamil Text Using Deep Learning Algorithms

Natural Language Processing (NLP) is a vast subject with applications in many fields in today’s modern world. The goal of NLP is to achieve human like language processing for a variety of activities or applications. The internet is full of textual data in many different languages. Although a large number of internet comments found in public spaces are often positive, a significant portion are toxic in nature. We first need to separate the good from the bad before classifying the different levels of toxicity. This will lessen any unintentional prejudice towards certain individuals or entity and lessen negativity on social media. Our primary goal is to identify, categorize, and analyze the toxicity that now plagues social media platforms. This study focuses on classifying Code Mixed Tamil text using deep learning algorithms. Tamil as a language has many obstacles to be overcome in this NLP task. Since Tamil’s grammar structure, specific features are unique and complex, it is actually hard to make a model that can consistently perform for any data from the language of Tamil. The agglutinative nature of Tamil is a major problem when it comes to tasks like classification since the context gets twisted when the single word is split into corresponding morphemes. Since there are many studies conducted on Code Mixed text of other languages with deep learning algorithms, this paper aims to find the effectiveness of XLNet and Bi-LSTM on Code-Mixed Tamil.