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

Computation and Language;Information Retrieval;Machine Learning;Machine Learning