Bi-Directional CNN-RNN Architecture with Group-Wise Enhancement and Attention Mechanisms for Cryptocurrency Sentiment Analysis

Abstract

As the cryptocurrency trading market has grown significantly in recent years, the number of comments related to cryptocurrency has increased tremendously in social media platforms. Due to this, sentiment analysis of the cryptocurrency-related comments has become highly desirable to give a comprehensive picture of peoples’ opinions about the trend of the market. In this regard, we perform cryptocurrency-related text sentiment classification using tweets based on positive and negative sentiments. For increasing the efficacy of the sentiment analysis, we introduce a novel deep neural network hybrid architecture which is composed of an embedding layer, a convolution layer, a group-wise enhancement mechanism, a bidirectional layer, an attention mechanism, and a fully connected layer. Local features are derived using a convolution layer, and weight values associated with intuitive features are developed using the group-wise enhancement mechanism. After feeding the improved context vector to the bidirectional layer to grab global features, the attention mechanism and the fully connected layer have been employed. The experimental findings indicate that the proposed architecture outperforms the state-of-the-art architectures with an accuracy value of 93.77%.