Examining Machine Learning Techniques in Business News Headline Sentiment Analysis

Abstract

Sentiment analysis is a natural language processing task that attempts to predict the opinion, feeling or view of a text. The interest in sentiment analysis has been rising due to the availability of a large amount of sentiment corpus and the enormous potential of sentiment analysis applications. This work attempts to evaluate different machine learning techniques in predicting the sentiment of the readers toward business news headlines. News articles report events that have happened in the world and expert opinions. These are factors that will affect market sentiment, and a headline can be considered as a summary of an article in a single sentence. In this study, we constructed a sentiment analysis corpus which consists of business news headlines. We examined two different approaches, namely text classification and recurrent neural network (RNN) in predicting the sentiment of a business news headline. For text classification approach, multi-layer perceptron (MLP) classifier, multinomial naïve Bayes, complement naïve Bayes and decision trees were experimented. On the other hand, for the RNN approach, we evaluated the typical RNN architecture and the encoder-decoder architecture in predicting the sentiment.