Mutual Information Approach For Sentiment Analysis Using Deep Machine Learning Convolution Neural Network (CNN) Model

Abstract

Sentiment Analysis is a field of Natural Language Processing which addresses the problem of extracting sentiment or, more generally, opinion from text. Obtaining deeper insights on that topic can be very valuable for a range of fields such as finance, marketing, politics and business. Previous research has shown how sentiment and public opinion can affect stock markets, product sales, polls as well as public health. This thesis researches the message sentiment polarity classification problem in Twitter aiming to classify messages based on the polarity of the sentiment towards a specific topic, where the tweets and the topics are always given. The dataset analysed and the evaluation metrics considered are provided conference and the 4th task about "Sentiment Analysis in Twitter". This task includes five subtasks, two of which were eventually engaged in this research according to the implemented approach. First, subtask B is a binary classification task, where the goal is to classify messages into two classes, positive and negative regarding the sentiment towards the topic. Following, subtask C where the target is to classify messages in a five-scale sentiment polarity from highly negative, negative, neutral, positive to highly positive, based on the sentiment towards a given topic. We are implemented and experimented with two deep learning models for both subtasks; a Convolutional Neural Network (CNN) and a state-of-the-art Recurrent Neural Network with context attention CNN LSTM model. We compare both models to the baselines of the challenge and show that the CNN LSTM outperforms the other models, in both subtasks, with all evaluation measures but one.