# LSTM Based Sentiment Analysis for Cryptocurrency Prediction

arXiv: https://arxiv.org/abs/2103.14804 · HTML: https://mlsanity.com/paper/2103.14804/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 27 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [FAHT: An Adaptive Fairness-aware Decision Tree Classifier](https://mlsanity.com/paper/1907.07237/) (2019)
- [Disentangled Dynamic Graph Deep Generation](https://mlsanity.com/paper/2010.07276/) (2020)
- [A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification](https://mlsanity.com/paper/1808.08315/) (2018)
- [Using Machine Learning to Automate Mammogram Images Analysis](https://mlsanity.com/paper/2012.03151/) (2020)
- [A Data-driven Human Responsibility Management System](https://mlsanity.com/paper/2012.03190/) (2020)

## Built on by (most cited first, 14 of 14)

- [FARF: A Fair and Adaptive Random Forests Classifier](https://mlsanity.com/paper/2108.07403/) (2021)
- [Longitudinal Fairness with Censorship](https://mlsanity.com/paper/2203.16024/) (2022)
- [Fairness Amidst Non-IID Graph Data: A Literature Review](https://mlsanity.com/paper/2202.07170/) (2022)
- [Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory](https://mlsanity.com/paper/2107.01671/) (2021)
- [Interpretable Visual Understanding with Cognitive Attention Network](https://mlsanity.com/paper/2108.02924/) (2021)
- [Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences](https://mlsanity.com/paper/2201.11650/) (2022)
- [Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility](https://mlsanity.com/paper/2202.08967/) (2022)
- [Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization](https://mlsanity.com/paper/2203.05673/) (2022)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [A Generic Knowledge Based Medical Diagnosis Expert System](https://mlsanity.com/paper/2110.04439/) (2021)
- [Improving Specificity in Mammography Using Cross-correlation between Wavelet and Fourier Transform](https://mlsanity.com/paper/2201.08385/) (2022)
- [Attention Mechanism based Cognition-level Scene Understanding](https://mlsanity.com/paper/2204.08027/) (2022)
- [Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models](https://mlsanity.com/paper/2409.02836/) (2024)
- [crypto price prediction using lstm+xgboost](https://mlsanity.com/paper/2506.22055/) (2025)

## Abstract (arXiv)

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.