Crypto-currencies narrated on tweets: a sentiment analysis approach

Abstract

Purpose Crypto-currencies, decentralized electronic currencies systems, denote a radical change in financial exchange and economy environment. Consequently, it would be attractive for designers and policy-makers in this area to make out what social media users think about them on Twitter. The purpose of this study is to investigate the social opinions about different kinds of crypto-currencies and tune the best-customized classification technique to categorize the tweets based on sentiments. Design/methodology/approach This paper utilized a lexicon-based approach for analyzing the reviews on a wide range of crypto-currencies over Twitter data to measure positive, negative or neutral sentiments; in addition, the end result of sentiments played a training role to train a supervised technique, which can predict the sentiment loading of tweets about the main crypto-currencies. Findings The findings further prove that more than 50 per cent of people have positive beliefs about crypto-currencies. Furthermore, this paper confirms that marketers can predict the sentiment of tweets about these crypto-currencies with high accuracy if they use appropriate classification techniques like support vector machine (SVM). Practical implications Considering the growing interest in crypto-currencies (Bitcoin, Cardano, Ethereum, Litcoin and Ripple), the findings of this paper have a remarkable value for enterprises in the financial area to obtain the promised benefits of social media analysis at work. In addition, this paper helps crypto-currencies vendors analyze public opinion in social media platforms. In this sense, the current paper strengthens our understanding of what happens in social media for crypto-currencies. Originality/value For managers and decision-makers, this paper suggests that the news and campaign for their crypto in Twitter would affect people’s perspectives in a good manner. Because of this fact, the firms, investing in these crypto-currencies, could apply the social media as a magnifier for their promotional activities. The findings steer the market managers to see social media as a predictor tool, which can analyze the market through understanding the opinions of users of Twitter.

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

“…Similarly, Sattarov – (2020) obtain 0.62 accuracy when predicting BTC price using sentiment analysis. Overall, the superiority of the SVM forecast is in line with previous studies which support SVM’s, such as Ismail et al (2020), Rouhani and Abedin (2020) and Mallqui and Fernandes (2019).…”

“…Lamon et al (2017) found logistic regression to perform best in using tweets to predict 44% of price increases and 62% of price falls. Likewise, Rouhani and Abedin (2020) found that more than 50% of positive beliefs about cryptos with the SVM outperform other ML classifiers. Last, but not least, Mai et al (2018) reported that social media sentiment is an important predictor to determine BTC's price, with, however varying impact of social media information.…”

Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach

“…Also, through sentiment analysis in Abraham et al (2018), it is shown that the tweet sentiments are highly correlated with cryptocurrency price movements. In a similar study, Rouhani & Abedin (2019) show that Ripple has the highest percentage of positive tweets (52%), and Bitcoin receives the highest percentage of negative tweets (27%) among all the cryptocurrencies by analysing around five million tweets. The predictive power of social media sentiment analysis is studied in Kraaijeveld & De Smedt (2020) and Kim et al (2021), and it is shown that Twitter sentiment has predictive power for price returns of Bitcoin, Bitcoin Cash, and Litecoin, and cryptocurrency markets are more responsive to positive social sentiment when it is in a downward trend.…”

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

“…Identification of the most impactful tweets through metrics such as retweets, comments, and high impact authors. Several authors have performed social media analysis in relation with citizen and financial market expectations, identifying the topics citizens were most interested in, based on the number of retweets (Romelli et al, 2022), doing Twitter sentiment analysis on cryptocurrencies (Rouhani & Abedin, 2019) or performing a literature review of the capacity of Twitter as an analytical and prognostic platform (Cano-Marin et al, 2022).…”

“…Several research studies exist on digital currencies and sentiment analysis of digital currencies on social media: machine learning techniques were employed, to demonstrate through empirical evidence that the use of combined Twitter data is valuable in forecasting the cryptocurrency volatility, return on investment and trading volume, with a particular emphasis on Bitcoin (Shen et al, 2019). Research has been performed on sentiment analysis to study cryptocurrencies from Twitter data and classify tweets as positive, neutral and negative, with the purpose of identifying pattern and pattern changes in public sentiment about cryptocurrencies, adding to the literature on how public perceptions and citizen sentiment about cryptocurrencies can be shaped (Rouhani & Abedin, 2019).…”