Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models

With the popularity of social networks, and e-commerce websites, sentiment has become a more active area of research in the past few years. On a level, sentiment analysis tries to understand the public opinion about a product or topic, or trends from reviews or tweets. Sentiment analysis an important role in better understanding customer/user opinion, and also social/political trends. There has been a lot of previous works for analysis, some based on hand-engineering relevant textual features, others based on different neural network architectures. In this work, we a model based on an ensemble of long-short-term-memory (LSTM), and neural network (CNN), one to capture the temporal information of data, and the other one to extract the local structure thereof. Through results, we show that using this ensemble model we can outperform individual models. We are also able to achieve a very high accuracy rate to the previous works.