Text sentiment analysis using deep convolutional networks

Abstract

The ability to process language is a difficult task. A computer system that can extract meaningful information from raw text data is considered to be equipped with intelligent behaviour. Sentiment analysis is a form of information extraction of growing research and commercial interest, especially because of the exponential increase of data in recent years. This paper presents a machine learning approach built upon Neural Networks that equips a computer system with the ability to learn how to detect sentiment in a given text and to correctly classify previously unencountered text with respect to predefined sentiment categories. The developed approach does not rely on any hard-coded parameters. Rather, a model is learned through a training procedure performed on raw data alone with the objective of searching to identify patterns and discovering feature predictors that allow the algorithm to classify text into positive, negative, or neutral sentiments, while additionally focusing on nuances such as humorous or sarcastic text. Experimental validation shows promising results. The learning algorithm is fed with high-volume textual data extracted from the Twitter social media platform. The trained model is then tested on a separate dataset, previously unknown by the model. The classification algorithm is able to predict the sentiment associated with a given text with high accuracy.