Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)

Abstract

Lexicon-based sentiment analysis in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts effectively. Although lexicon-based methods are simple to implement and fast to operate on textual data, they require considerable manual annotation efforts to create, maintain, and update the lexicons. These methods are also considered inferior to the deep learning-based approaches, such as transformer models, which have become dominant in various natural language processing (NLP) tasks due to their remarkable performance. However, their efficacy comes at a cost: these models require extensive data and computational resources for both training and testing. Additionally, they involve significant prediction times, making them unsuitable for real-time production environments or systems with limited processing capabilities. In this paper, we introduce a novel methodology named eXplainable Lexicons (XLex) that combines the advantages of both lexicon-based methods and transformer models. We propose an approach that utilizes transformers and SHapley Additive exPlanations (SHAP) for explainability to automatically learn financial lexicons. Our study presents four main contributions. Firstly, we demonstrate that transformer-aided explainable lexicons can enhance the vocabulary coverage of the benchmark Loughran-McDonald (LM) lexicon. This enhancement leads to a significant reduction in the need for human involvement in the process of annotating, maintaining, and updating the lexicons. Secondly, we show that the resulting lexicon outperforms the standard LM lexicon in sentiment analysis of financial datasets. Our experiments show that XLex outperforms LM when applied to general financial texts, resulting in enhanced word coverage and an overall increase in classification accuracy by 0.431. Furthermore, by employing XLex to extend LM, we create a combined dictionary, XLex+LM, which achieves an even higher accuracy improvement of 0.450. Thirdly, we illustrate that the lexicon-based approach is significantly more efficient in terms of model speed and size compared to transformers. Lastly, the proposed XLex approach is inherently more interpretable than transformer models. This interpretability is advantageous as lexicon models rely on predefined rules, unlike transformers, which have complex inner workings. The interpretability of the models allows for better understanding and insights into the results of sentiment analysis, making the XLex approach a valuable tool for financial decision-making.

Keywords

• Machine learning
• natural language processing
• text classification
• sentiment analysis
• finance
• lexicons