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Related Experiment Video

Updated: Aug 8, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Market prediction using machine learning based on social media specific features.

Satoshi Sekioka¹, Ryo Hatano¹, Hiroyuki Nishiyama¹

¹Department of Industrial Administration, Graduate School of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda Chiba, Japan.

Artificial Life and Robotics

|February 28, 2023

Summary

Social media sentiment analysis using Sentence-BERT and LightGBM can predict cryptocurrency price changes. Linguistic features from tweets improve the accuracy of forecasting sudden market movements.

Keywords:

Cryptocurrency Machine learning Natural language processing

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Area of Science:

Computational Finance
Natural Language Processing
Machine Learning

Background:

Social media sentiment significantly impacts financial markets, including virtual currencies.
Predicting sudden price changes in cryptocurrencies remains a challenge.

Purpose of the Study:

To investigate the efficacy of Twitter data and natural language expressions for predicting virtual currency market information.
To develop a model for forecasting sudden price changes (drops, rises, or stability) in cryptocurrencies.

Main Methods:

Feature extraction from tweets using Sentence-BERT.
Training a LightGBM classifier with these linguistic features.
Classification task with three labels: sudden drop, sudden rise, or no sudden change.

Main Results:

Linguistic features derived from tweets enhance the prediction of cryptocurrency price changes.
The proposed method demonstrates the advantage of using natural language expressions for market trend prediction.

Conclusions:

Social media sentiment analysis is a valuable tool for understanding and predicting cryptocurrency market dynamics.
Integrating linguistic features into machine learning models improves the accuracy of financial market forecasting.