Effective Vietnamese Sentiment Analysis Model Using Sentiment Word Embedding and Transfer Learning

Sentiment analysis is one of the most popular fields in NLP, and with the development of computer software and hardware, its application is increasingly extensive. Supervised corpus has a positive effect on model training, but these corpus are prohibitively expensive to manually produce. This paper proposes a deep learning sentiment analysis model based on transfer learning. It represents the sentiment and semantics of words and improves the effect of Vietnamese sentiment analysis model by using English corpus. It generated semantic vectors through Word2Vec, an open-source tool, and built sentiment vectors through LSTM with attention mechanism to get sentiment word vector. With the method of sharing parameters, the model was pre-training with English corpus. Finally, the sentiment of the text was classified by stacked Bi-LSTM with attention mechanism, with input of sentiment word vector. Experiments show that the model can effectively improve the performance of Vietnamese sentiment analysis under small language materials.