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

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