Predicting Bitcoin Trends Through Machine Learning Using Sentiment Analysis with Technical Indicators

Abstract

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts. An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost). It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results. Thus, the input features used in the paper can be applied on Bitcoin price prediction. Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments.

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

“…Referring to a study by Wu et al 36 , sentiment measurements were calculated as the difference between the number of negative and positive posts in a specific dataset. where represents the number of positive news articles and represents the number of negative articles on day t. The range of values for the sentiment index was between −1 and 1 25 . If the sentiment index value approaches −1, it suggests a negative tone in the news for that date.…”

“…As a result, the experimental result demonstrated better accuracy compared to not using technical indicators. Jung et al 25 combined technical and sentiment indicators to predict Bitcoin price trends using the RSI, SMA, EMA, MACD, signal, Stochastic RSI, and Stochastic Oscillator indices. As a result, considering 11 technical indicators was found to be effective, with XGBoost exhibiting a prediction performance of 90.57%.…”

Opportunities and challenges of integrating artificial intelligence in China's elderly care services

“…Jung et al 18 gathered data from Reddit and LexisNexis spanning over four years to assess the performance of six machine learning techniques. These techniques incorporated technical and sentiment indicators, along with post volume, to predict Bitcoin price trends.…”

Exploring Consumer Sentiment on Central Bank Digital Currencies: A Twitter Analysis from 2021 to 2023

“…Central banks have been increasingly engaging themselves with citizens on social media platforms (Facebook, LinkedIn, Twitter) and announcements related to banknotes were retweeted the most (Romelli et al, 2022) compared to eight other studied topics (related to monetary policy, data releases, exchange rate information, bulletins, research, speeches and interviews, conferences) because the target audience evolved over the past years. Twitter sentiment analysis was also used to predict Bitcoin trends (Jung et al, 2023) and to analyze transaction volume changes in the crypto currency market (See & Ulpah, 2022). A comprehensive survey on sentiment analysis and opinion mining techniques for cryptocurrencies was also conducted, exploring various methods and applications for analyzing digital currency sentiment (Aziz & Hussain, 2020).…”