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Sentiment analysis of financial news articles using performance indicators

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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.

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Authors and Affiliations

  1. Information Systems Area, Indian Institute of Management Ahmedabad, Ahmedabad, India

    Srikumar Krishnamoorthy

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  1. Srikumar Krishnamoorthy
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Correspondence to Srikumar Krishnamoorthy.

Appendix

Appendix

1.1 A. Parsing sentences using the NLTK toolkit

figure c

Steps:

  1. 1.

    Parse the sentence with a regular expression parser using the grammar specified above

  2. 2.

    If a match for ’NPJJ’ tree pattern is found:

    1. (a)

      Traverse the subtree to get the combination of NP (potential performance indicator) and JJ/RB/VB (potential directionality word). Look up the dictionary for the matching indicator and directionality word.

    2. (b)

      If the combination of NP and JJ/RB/VB are not found, check for presence of individual words (either performance indicator or directionality word) in the dictionary

    3. (c)

      Tag the sentence based on the identified matches.

1.2 B. Parsing numeric values to determine directionality

Preconditions:

  1. 1.

    If a sentence has not been tagged with combination of performance indicators and directionality using the parse rules specified in Section A above.

  2. 2.

    The sentence contains terms like compared to, versus, down from, up from #common sentences where one is likely to observe multiple numeric values without the use of directional words.

Example sentence: Operating profit margin was 8.3%, compared to 11.8%

                              a year earlier.

Expected tag output: LagInd::DOWN

figure d

Steps:

  1. 1.

    Parse the sentence with a regular expression parser using the grammar specified above

  2. 2.

    If a match for ’NPJJ’ tree pattern is found:

    1. (a)

      Traverse the subtree to get the combination of NP, CD, CD to extract the performance indicator and numeric values. The numeric values are analyzed to determine the directionality (UP/DOWN)

    2. (b)

      Tag the sentence based on the identified matches.

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Krishnamoorthy, S. Sentiment analysis of financial news articles using performance indicators. Knowl Inf Syst 56, 373–394 (2018). https://doi.org/10.1007/s10115-017-1134-1

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  • DOI: https://doi.org/10.1007/s10115-017-1134-1

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