# Contextual Sentence Analysis for the Sentiment Prediction on Financial Data

arXiv: https://arxiv.org/abs/2112.13790 · HTML: https://mlsanity.com/paper/2112.13790/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 27 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)
- [Sentiment Analysis of Financial News Articles using Performance Indicators](https://mlsanity.com/paper/1811.11008/) (2018)
- [Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines](https://mlsanity.com/paper/1704.00939/) (2017)

## Built on by (most cited first, 2 of 2)

- [Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification](https://mlsanity.com/paper/2309.02189/) (2023)
- [Forecasting Four Business Cycle Phases Using Machine Learning: A Case Study of US and EuroZone](https://mlsanity.com/paper/2405.17170/) (2024)

## Abstract (arXiv)

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.