A study of sentiment analysis using deep learning techniques on Thai Twitter data

Abstract

Sentiment analysis is very important for social listening, especially, when there are millions of Twitter users in Thailand nowadays. Almost all prior works are based on classical classification techniques, e.g., SVM, Naïve Bayes, etc. Recently, the deep learning techniques have shown promising accuracy in this domain on English tweet corpus. In this paper, we propose the first study that applies deep learning techniques to classify sentiment of Thai Twitter data. There are two deep learning techniques included in our study: Long Short Term Memory (LSTM) and Dynamic Convolutional Neural Network (DCNN). A proper data preprocessing has been conducted. Moreover, we also investigate an effect of word orders in Thai tweets. The results show that the deep learning techniques significantly outperform many classical techniques: Naïve Bayes and SVM, except Maximum Entropy.