Multi-channel word embeddings for sentiment analysis

Sentiment analysis (SA) is widely applied in practical applications and known as emotion AI or opinion mining. In fact, SA tasks are really hard even for human; there are many factors that can affect the overall performance such as the length of the text, idiom, metaphor, or slang. In this paper, mainly proposed multi-channel word embeddings for SA consists of three parts: improving word representation, applying attention mechanism, and applying the state-of-the-art deep learning models. Particularly, a better representation, first, allows our method to achieve accurate and higher performance. Second, the attention mechanism enhances the model ability in focusing on the place from where the useful and important information can be extracted. In final, in order to benchmark our proposed method, we implement deep learning models such as CNN, RNN, CNN variants. The experimental results show that our method achieved a higher performance compared to baseline methods. The experiment results highlighted the main contributions based on better word representation of multi-channel pre-trained word embeddings were shown. In addition, this model focuses on the words that contain useful and important information, and a higher accuracy was found compared to the previous models.