Aspect-based Financial Sentiment Analysis with Deep Neural Networks

Abstract

Aspect-based financial sentiment analysis, which aims to classify the text instance into a pre-defined aspect class and predict the sentiment score for the mentioned target. In this paper, we propose a neural network model, Attention-based LSTM model with the Aspect information (ALA), to solve the financial opinion mining problem introduced by the WWW 2018 shared task. The proposed neural network model can adapt to the financial dataset so that the neural network can effectively understand the semantic information of the short text. We evaluate our model with the 10-fold cross-validation, and we compare our model with a variety of related deep neural network models.