Decision Support Machine - A Hybrid Model for Sentiment Analysis of News Headlines of Stock Market

Abstract

Forecasting and making speculations about the financial market is intriguing and enticing for many of us. Predicting sentiments in the field of finance is a difficult thing as there is a special language that is used in financial markets and the data is unlabeled. Generalized models are not sufficient because the words that are used in financial markets have a completely different meaning when compared to their regular use. This paper represents the study of the stock price fluctuations and forecasting of the future stock prices using financial news about the big IT giants. NLP techniques should be applied to extract the correct sentiments out of the statements. This paper proposes a hybrid Machine Learning model DSM i.e. Decision Support Machine based on Support Vector Machine and Decision Tree. In this study news headlines dataset is preprocessed and then used for making predictions. The results show that the proposed model DSM got an accuracy of 79.75%. Results are then compared with the real-time stock market data for the same time duration, thus giving us a better picture of the actual changes. DSM is also compared with BERT, TextBlob, Decision Tree, Naïve Bayes, NLTK-Vader, SVM and KNN. The proposed model can further be extended if more datasets associated with investors’ sentiments can be used for training.