  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.
