Intent Classification on Myanmar Social Media Data in Telecommunication Domain Using Convolutional Neural Network and Word2Vec

Abstract

Nowadays, people widely use social media and spend more time on that. Intentions behind users' generated content can be ranged from social good to feedbacks about the service or product of a company. With the help of deep learning models, users' intentions can classify more accurately. This paper focuses on the intent classification of users' generated comments on social media posted in Myanmar text. In this paper, Word2Vec is used to convert words into vector representations, which will be input for the Convolutional Neural Networks (CNN) to classify the users' comments to one of the pre-defined classes. Continuous Bag of Words (CBOW) architecture is used to train Word2Vec model. The proposed model's comparative experiment was performed on the baseline Recurrent Neural Network (RNN) model with a single recurrent layer. Facebook is a target social medial platform. Content from social media are domain-independent and makes it difficult to classify. So, in the proposed model, telecommunication is the target social media domain. Users' comments from that domain are regarded as feedbacks and collected as training and testing data for the model. According to the experimental result, the proposed model outperforms the average F-Score value of 0.94 over RNN.