N-Gram Feature for Comparison of Machine Learning Methods on Sentiment in Financial News Headlines

Abstract

Sentiment analysis is currently widely used in natural language processing or information retrieval applications. Sentiment analysis analysis can provide information related to outstanding financial news headlines and provide input to the company. Positive sentiment will also have a good impact on the development of the company, but negative sentiment will damage the company's reputation. This will affect the company's development. This study compares machine learning methods on financial news headlines with n-gram feature extraction. The purpose of this study was to obtain the best method for classifying the headline sentiment of the company's financial news. The machine learning methods compared are Multinomial Naïve Bayes, Logistic Regression, Support Vector Machine, multi-layer perceptron (MLP), Stochastic Gradient Descent, and Decision Trees. The results show that the best method is logistic regression with a percentage of f1-measure, precision, and recal of 73.94 %, 73.94 %, and 74.63 %. This shows that the n-gram and machine learning features have successfully carried out sentiment analysis.