Sentiment analysis of financial news articles using performance indicators

Abstract

Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights. Sentiment analysis and opinion mining [31,37] has received significant attention in the literature due to its wide applicability in business, management and social science disciplines. It has been effectively applied in domains such as movies [31,37], product reviews [5], travel reviews [7,37] and finance [2,11,23,24,34,35]. This is an accepted version of the article published in Knowledge and Information Systems Journal. The final authenticated version is available online at: https://doi.