LSTM Based Sentiment Analysis for Cryptocurrency Prediction

TL;DR

This paper presents an LSTM-based model that analyzes Chinese social media sentiment to predict cryptocurrency price movements, outperforming existing models in accuracy and recall.

Contribution

It introduces a novel approach combining Chinese social media sentiment analysis with LSTM neural networks for cryptocurrency prediction, including a new crypto-specific sentiment dictionary.

Findings

Outperforms state-of-the-art autoregressive models by 18.5% in precision.

Achieves 15.4% higher recall in price trend prediction.

Effectively captures sentiment from Sina-Weibo for financial forecasting.

Abstract

Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide valuable sentiment information to predict the price fluctuation of the cryptocurrency. This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. While previous work has been developed to analyze sentiment in English social media posts, we propose a method to identify the sentiment of the Chinese social media posts from the most popular Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-specific sentiment dictionary, and propose a long short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency price movement to predict the price trend for future time frames. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.