Contextual Sentence Analysis for the Sentiment Prediction on Financial Data

TL;DR

This paper introduces a hierarchical Transformer model that combines contextual sentence analysis with sentiment dictionaries to improve sentiment prediction accuracy on financial texts, outperforming previous systems.

Contribution

The paper presents a novel hierarchical Transformer architecture that integrates sentiment dictionaries with fine-tuned RoBERTa for enhanced financial sentiment analysis.

Findings

Outperforms SemEval-2017 best systems

Combining dictionaries with Transformers improves accuracy

Achieves reliable sentiment scoring in financial texts

Abstract

Newsletters and social networks can reflect the opinion about the market and specific stocks from the perspective of analysts and the general public on products and/or services provided by a company. Therefore, sentiment analysis of these texts can provide useful information to help investors trade in the market. In this paper, a hierarchical stack of Transformers model is proposed to identify the sentiment associated with companies and stocks, by predicting a score (of data type real) in a range between -1 and +1. Specifically, we fine-tuned a RoBERTa model to process headlines and microblogs and combined it with additional Transformer layers to process the sentence analysis with sentiment dictionaries to improve the sentiment analysis. We evaluated it on financial data released by SemEval-2017 task 5 and our proposition outperformed the best systems of SemEval-2017 task 5 and strong baselines. Indeed, the combination of contextual sentence analysis with the financial and general sentiment dictionaries provided useful information to our model and allowed it to generate more reliable sentiment scores.