Classification of Bangla News Articles Using Bidirectional Long Short Term Memory

Classification is a method of assigning input vectors to one of the discrete classes. This problem can be used to identify related content such as E-commerce, news agencies, content curators, blogs, directories, and likes can use automated technologies. In this paper, we have proposed a method of classification using bi-directional LSTM to classify the Bangla news headline. We have used Bangla stop word corpus to removing stop words to get a better result in our method of classification. We have used Gensim and fastText model to vectorized our text to compatible with our machine learning model. We have built a dataset that contains around 10 lakh articles from the different renowned newspapers of Bangladesh and 8 different categories. Then we trained this data in 3 different models. Among those models, Bi-LSTM has achieved 85.14 percent accuracy, which is better than any other method.