تقرير
Cryptocurrency Price Prediction using Twitter Sentiment Analysis
| العنوان: | Cryptocurrency Price Prediction using Twitter Sentiment Analysis |
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| المؤلفون: | GB, Haritha, B, Sahana N. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Quantitative Finance |
| مصطلحات موضوعية: | Quantitative Finance - Statistical Finance, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning |
| الوصف: | The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user's following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%. Comment: 10 pages, NIAI 2023 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.5121/csit.2023.130302 |
| URL الوصول: | http://arxiv.org/abs/2303.09397 |
| رقم الأكسشن: | edsarx.2303.09397 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.5121/csit.2023.130302 |
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