A Brief Introduction of the Text Classification Methods

Text classification is basically to categorize text data into different groups. Text classification has been applied in various domains, including news filtering and organization, document organization and retrieval, opinion mining, and email classification and spam filtering. However, in the Era of Big Data, in which text data is generated every second, it is almost impossible to classify text manually. This paper will briefly talk about common task classification methods with greater emphasis on DL classification models, CNN and RNN. CNN is good at extracting word pattern of the text. A classification is done by detecting particular words' presence and location. RNN converts the text into a word embedding vector and processes the vector as a sequential data. It makes a classification by a series of computation based on the word sequence. This paper will also analyze several research papers which further explore the nature of CNN and RNN classification models. Finally, it will address the problems that current text classification models face.