Exploring the Effect of Word Embeddings and Bag-of-Words for Vietnamese Sentiment Analysis

Abstract

In the field of natural language processing, word embeddings and bag of words are two objects used to represent initial features for many machine learning models. Bag of words is known to be widely used and suitable for traditional models such as SVM, logistic regression, random forest. Meanwhile, word embeddings are used suitable for deep learning models that designed based on Convolutional neural network (CNN) and Long short-term memory (LSTM). Many sentiment analysis studies have effectively used word embeddings. However, they have ignored the role of bag of words in their proposed model, this paper proposes a CNN model that simultaneously exploits the effectiveness of word embeddings and bag of words for vietnamese sentiment analysis. In the experiment, we used 4009 actual textual reviews in the data domain of mobile phone products, the experimental results have demonstrated the ability of the proposed model as well as the role of bag of words.