Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks.

Sentiment analysis in social networks plays an important role in different areas, and one of its main tasks is to determine the polarity of sentiments about many things. In this paper, our goal is to create a supervised machine learning model for predicting the polarity of users' sentiments, based solely on their textual history, about a predefined topic. The proposed approach is based on neural network architectures: the long short term memory (LSTM) and the convolutional neural networks (CNN). To experiment our system, we have purposely created a collection from SemEval-2017 data. The results revealed that our approach outperforms the comparison approach.