Sentiment analysis of financial news articles using performance indicators

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

Srikumar Krishnamoorthy


Scholarcy highlights

  • Sentiment analysis and opinion mining has received significant attention in the literature due to its wide applicability in business, management and social science disciplines
  • Some of the common dictionaries used in the financial sentiment analysis literature include Harvard GI, MPQA, Sentiwordnet, SenticNet, SentiStrength2, LM, and Financial Polarity Lexicon
  • We aim to improve the state-of-the-art in domain specific dictionary based financial sentiment analysis
  • We present a new approach to perform financial sentiment analysis based on performance indicators and conduct rigorous experiments to assess its usefulness
  • In the set of experiments, we study the influence of performance indicators and financial sentiment words on sentiment prediction
  • The results reveal that the proposed method is significantly better compared to other state-of-the-art methods in all of the datasets studied
  • This paper examined the use of performance indicators for predicting sentiments from financial texts
  • The results are clearly in alignment with the way humans interpret financial texts and make decisions

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