Understanding Meme Coin Trends Through Sentiment Analysis

Abstract

This study explores the use of sentiment analysis and machine learning models to predict the market trends of meme coins. By analyzing social media sentiment and financial metrics, the research achieved a 74% accuracy rate in forecasting both bullish and bearish market movements. Despite these promising results, challenges such as fluctuating sentiment and the quality of data persist. Future studies should aim to incorporate a broader range of data sources and advanced machine learning techniques to improve the precision of predictions.

Introduction

Meme coins (e.g., Dogecoin, Shiba Inu) are a unique class of cryptocurrencies, highly influenced by social media trends and public sentiment, rather than intrinsic value or technological fundamentals. Their extreme volatility makes them ideal candidates for sentiment-driven price prediction.

Research Objective

To explore how social media sentiment, analyzed through NLP techniques, combined with machine learning (ML) models like XGBoost, can be used to predict bullish or bearish trends in meme coin prices.

Key Components and Methods

1. Sentiment Analysis in Finance & Crypto:

   • Prior studies show Twitter and Reddit sentiment correlates with stock and crypto price changes.

   • Meme coins are particularly sensitive to online hype and celebrity influence.

2. Machine Learning Use:

   • XGBoost and other tree-based models are effective for handling large, non-linear financial datasets.

   • Models are trained using sentiment scores + financial indicators.

3. Methodology Overview:

   • Data Sources: Twitter, Reddit (social sentiment) + crypto market data (volume, prices).

   • Preprocessing: Cleaned social text, applied VADER/TextBlob for sentiment scoring.

   • Feature Engineering: Created features like “sentiment volatility.”

   • Model Training: XGBoost model trained with hyperparameter tuning (grid search).

   • Evaluation: Used accuracy, precision, recall, F1-score with cross-validation.

Results and Findings

• Accuracy: The model predicted bullish trends well but failed to detect bearish trends, indicating an imbalance in sentiment classification.

• Feature Importance: Some features (like "f0") had disproportionately high impact on prediction accuracy.

• Cross-Validation: Helped confirm the model’s robustness across different data subsets.

Challenges Identified

• Unstructured Text: Meme coin discussions often include slang, emojis, and inconsistent language, challenging for standard NLP tools.

• Sentiment Volatility: Rapid shifts due to viral events or celebrity tweets make real-time prediction difficult.

• Data Quality: Sentiment models require high-quality, diverse, and timely data to be effective.

Conclusion

This study effectively highlights the capability of sentiment analysis and machine learning techniques, particularly XGBoost, to predict market trends for meme coins within the cryptocurrency market. By combining social media sentiment with financial data, the research achieved an encouraging accuracy rate of 74%, showcasing the potential of sentimentbased forecasting models. Nonetheless, challenges such as interpreting informal sentiment and dealing with high market volatility persist. Future work should aim to improve sentiment analysis methods, incorporate diverse data sources, and address ethical implications. This research lays the foundation for more advanced, data-driven models for cryptocurrency market analysis, offering valuable insights for traders and investors.

References

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Copyright

Copyright © 2025 Pratham Pise, Mohit Salunkhe, Prof. Pallavi Thakur. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.