English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Lexicon-based sentiment analysis (SA) in finance leverages specialized,manually annotated lexicons created by human experts to extract sentiment fromfinancial texts. Although lexicon-based methods are simple to implement andfast to operate on textual data, they require considerable manual annotationefforts to create, maintain, and update the lexicons. These methods are alsoconsidered inferior to the deep learning-based approaches, such as transformermodels, which have become dominant in various NLP tasks due to their remarkableperformance. However, transformers require extensive data and computationalresources for both training and testing. Additionally, they involve significantprediction times, making them unsuitable for real-time production environmentsor systems with limited processing capabilities. In this paper, we introduce anovel methodology named eXplainable Lexicons (XLex) that combines theadvantages of both lexicon-based methods and transformer models. We propose anapproach that utilizes transformers and SHapley Additive exPlanations (SHAP)for explainability to learn financial lexicons. Our study presents four maincontributions. Firstly, we demonstrate that transformer-aided explainablelexicons can enhance the vocabulary coverage of the benchmark Loughran-McDonald(LM) lexicon, reducing the human involvement in annotating, maintaining, andupdating the lexicons. Secondly, we show that the resulting lexicon outperformsthe standard LM lexicon in SA of financial datasets. Thirdly, we illustratethat the lexicon-based approach is significantly more efficient in terms ofmodel speed and size compared to transformers. Lastly, the XLex approach isinherently more interpretable than transformer models as lexicon models rely onpredefined rules, allowing for better insights into the results of SA andmaking the XLex approach a viable tool for financial decision-making.

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