Impact of Twitter Sentiment Related to Bitcoin on Stock Price Returns

Feda Hassan Jahjah,Muhanad Rajab

doi:10.31026/j.eng.2020.06.05

Abstract

Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.

Highlights

Sentiment analysis is generally used to identify the writer's feelings about a topic that can express an opinion or emotional state
A static dataset and SVM classification model were used taking into account the total number of tweets per day, the number of positive tweets per day, the number of negative tweets per day, and the polarity of positive or negative sentiment. (Sohangir et al, 2018) they applied several deep learning models such as Long-Short Term Memory Long-Short term memory Neural Network (LSTM), doc2vec and Convolutional neural networks to stock market opinions published in StockTwits, the results show that the deep learning model can be used effectively to analyze financial sentiments and that the Convolutional neural network is the best model To predict the sentiment of the authors in the StockTwits dataset. (Kar et al, 2017) introduced a sentiment analysis system to extract sentiment from microblogging and news headlines
We have presented a model that aim to combine Convolutional Neural Networks (CNN) and LSTM neural networks to achieve better performance on sentiment analysis tasks

Summary

Introduction

Sentiment analysis is generally used to identify the writer's feelings about a topic that can express an opinion or emotional state. Deep-learning applications have shown remarkable results in natural language processing including sentiment analysis across multiple data sets (Collobert et al, 2001). These models don’t need to be provided with predefined features, but they learn advanced features from the data set by themselves. The words are represented in a high-dimensional vector space, and the neural network does the feature extraction process Structures such as RNN are able to understand sentence structure efficiently. Models are trained using a wide range of pre-labeled data (class-specific) and using multi-layer neural network structures that learn properties directly from the data without the need for manual extraction of features Fig. 1. These filters are sequentially applied to different sections of the input

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Conclusion