Assessing Regression-Based Sentiment Analysis Techniques in Financial Texts

• Taynan Ferreira Universidade de São Paulo
• Francisco Paiva Universidade de São Paulo
• Roberto da Silva Universidade de São Paulo
• Angel de Paula Universidade de São Paulo
• Anna Costa Universidade de São Paulo
• Carlos Cugnasca Universidade de São Paulo

DOI: https://doi.org/10.5753/eniac.2019.9329

Resumo

Sentiment analysis (SA) is increasing its importance due to the enormous amount of opinionated textual data available today. Most of the researches have investigated different models, feature representation and hyperparameters in SA classification tasks. However, few studies were conducted to evaluate the impact of these features on regression SA tasks. In this paper, we conduct such assessment on a financial domain data set by investigating different feature representations and hyperparameters in two important models -- Support Vector Regression (SVR) and Convolution Neural Networks (CNN). We conclude presenting the most relevant feature representations and hyperparameters and how they impact outcomes on a regression SA task.

Palavras-chave: Machine Learning, Text and Web Mining, Natural Language Processing, Deep Learning

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