Handling data imbalance using CNN and LSTM in financial news sentiment analysis

Abstract

With a speedy development in Natural Language processing, the financial sector meets the demand of analyzing a large quantity of financial text data. Several recent research has focused on the subject of Financial Sentiment Analysis (FSA). In this article, we worked on sentiment analysis which is one of the most popular areas of natural language processing. We tried to use the sentiment analysis of news in the financial market, as sometimes news has a very strong impact on the stock market. We used the data of P. Malo [18] containing the 5,000 sentences of the finance news with labels of the sentiment. This study uses machine learning and deep learning algorithms as a research approach to develop a comprehensive comparative study on Financial News Sentiment Analysis that includes data sources. We compared the classification accuracy performance of machine learning and deep learning algorithms such as SVM, KNN, Decision Tree, Random Forest, XGBoost, CNN, and LSTM in a sentiment analysis of financial news. Our inspirations in the future direction such as handling data imbalance also discussed and applied for algorithms. The experiments demonstrate that the CNN algorithm, based on accuracy, consistently outperforms the other models in the performance of sentiment analysis of financial news.