Investigating an Individual’s Opinion on Social Media About the Cryptocurrency Market (original) (raw)
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Analyzing Cryptocurrency trends using Tweet Sentiment Data and User Meta-Data
arXiv (Cornell University), 2023
Cryptocurrency is a form of digital currency using cryptographic techniques in a decentralized system for secure peer-to-peer transactions. It is gaining much popularity over traditional methods of payments because it facilitates a very fast, easy and secure way of transactions. However, it is very volatile and is influenced by a range of factors, with social media being a major one. Thus, with over four billion active users of social media, we need to understand its influence on the crypto market and how it can lead to fluctuations in the values of these cryptocurrencies. In our work, we analyze the influence of activities on Twitter, in particular the sentiments of the tweets posted regarding cryptocurrencies and how it influences their prices. In addition, we also collect metadata related to tweets and users. We use all these features to also predict the price of cryptocurrency for which we use some regressionbased models and an LSTM-based model.
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Along with the fast-pace integration of social media into our everyday life, the same goes to the investment world. In such volatile and manipulative markets like cryptocurrencies', investor sentiment are easily influenced by Key Opinion Leaders (KOLs), fake news, and herding behaviors. We tackled this issue by analyzing the Tweets sentiment using VADER lexicon approach and cross-correlation between lagged tweets sentiment score, tweets volume, and hourly price changes of the four most mentioned altcoins on Twitter (BTC, ETH, XRP, DOGE). We found out that while some cryptocurrencies have been more efficient throughout the time, some are still heavily influenced by social media sentiment. Especially, the value of Dogecoin does not based on its intrinsic value or utility, but fully dependent upon social media sentiment.
Impacts of Positive and Negative Comments of Social Media Users to Cryptocurrency
Blockchain implementation brought several benefits to many areas. One of the usages of blockchain is in digital currencies. Digital currency (cryptocurrency) is a new era for the global financial system. Cryptocurrencies draw significant attention from researchers because of their advantages. Although there are several risks (e.g., speculation, 51% attack) related to cryptocurrency, billions of dollars are invested in them, because of their transparency, traceability, low transaction cost, and highly profitable potential. In December 2017, the most famous cryptocurrency, Bitcoin, has reached almost $20,000.00 per coin. Such short-term, high gain potential attracts many new small investors. However, speculative movements raise many questions related to the safety and privacy of investors, just to name a few. To understand public opinions about cryptocurrency and speculative movements to protect small investors financial interests, sentiment analysis can be done by using social media activities of individuals who are interested or investing in cryptocurrencies. It is also one of the essential steps in the analysis to understand the profiles of the users. Therefore, in this paper, we determine the attitudes of social network users by analyzing the positivity and negativity of the comments about six cryptocurrencies. Results show that the positivity is higher than negativity, and there exist relations between price changes and attitudes. However, relations vary according to currency types. The results and analysis, which are provided in this paper, help new investors and developers to obtain opinions of social network users who are interested or investing in cryptocurrency.
Cryptocurrency – Sentiment Analysis in Social Media
Acta Marisiensis. Seria Technologica, 2019
The paper proposes the exploration, identification and development of a Java solution for extracting the sentiment related to the cryptocurrencies phenomenon, from the content of the posts of certain popular social networks. Detecting the positive, neutral or negative character of the sentiment is adopted as a relevant method of establishing the nature of the human perception on the topical issue defined by cryptocurrencies.
Empirical Analysis Тowards the Effect of Social Media on Cryptocurrency Price and Volume
Bitcoin's value is highly dependent on the communities that use it. This network effect is true for all new technologies. Today's online communities are so large in population that both the Facebook user and Youtuber populations have surpassed the Chinese population. We take a big data approach using millions of samples of posts from Twitter, Telegram, and Reddit to study how and if social media platforms, the epitome of online communities, affect Bitcoin's price and volume as well as the price and volume of fifteen other top cryptocurrencies. We work in collaboration with Solume, a data centered fin-tech startup, as well as with Sentistrength, an opinion mining tool developed by researchers in the UK, to classify the sentiment of the millions of posts we study. We collected millions of posts related to 16 cryptocurrencies from November 2017 through August 2018 on an hourly basis and explore social media volume sentiment effect on these cryptocurrencies. Findings confirm that volumes of exchanged posts may predict the fluctuations of Bitcoin's price but mainly, they predict volume. We also find that Reddit and Telegram posts have greater impact on Bitcoin volume than Twitter. Results indicate that information about the use of social media platforms can assist in tracking real world behavior and may even predict real financial market trends.
Cryptocurrencies investment framework using sentiment analysis of Twitter influencers
Indonesian Journal of Electrical Engineering and Computer Science
In recent years, cryptocurrency technology has become an attractive area for investment due to its transparency, independence, and non-transactional nature. Many analysts and researchers talk daily on social media about the future of various cryptocurrencies. These ideas can significantly impact whether or not people are willing to invest. This paper provides a framework to help traders learn about the opinions of influential people and organizations in the field. Over the course of six months, the sentiment of more than 90 significant Twitter users was extracted for the proposed framework. In this study, we used the Vader open-source tool for sentiment analysis. This paper provides an excellent opportunity for investment through sentiment analysis of lesser-known or emerging cryptocurrencies. Also in this paper, we introduce the user importance factor to calculate the value of each tweet based on the number of retweets and comments. This factor shows the importance of their opinion...
Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis
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In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over $160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict t...
Using Data Mining Algorithm for Sentiment Analysis of Users’ Opinions About Bitcoin Cryptocurrency
2019
Cryptocurrency has turned out to be one of the most significant currencies in the recent times due to their secureness, ease and value. Among the other cryptocurrencies available in the market, Bitcoin cryptocurrency is the most valuable and famous currency. A large number of people discuss about the Bitcoin currency on the internet and social media platforms as well. These discussions help in determining the importance of Bitcoin in terms of users’ discussions about Bitcoin and can help in determining the value of Bitcoin in terms of people point of views about the topic. In this paper, sentiment analysis of the tweets of users on the topic of Bitcoin has been carried out. For this purpose, real-world twitter data set of Bitcoins is used. The data set has been divided into five separate sections for better comparative analysis, including overall extensive data analysis regarding tweets, retweets, tweets with mentions, tweets containing external links and also about the users who di...
LONG AND SHORT-TURN RELATIONSHIP BETWEEN TWITTER SENTIMENT ANALYSIS AND BITCOIN PRICES
7. IERFM ECONOMIC RESERACH AND FINANCIAL MARKETS CONGRESS WITH INTERNATIONAL PARTICIPATION PROCEEDING BOOK, 2022
The role of social media in cryptocurrency pricing has gained significant importance in recent years. In this regard, this study uses Twitter data as a social media output to examine the relationship between sentiment analysis results obtained from tweets related to Bitcoin and Bitcoin's price in both the long and short terms. The direction of the relationship between the variables was also examined. Sentiment analysis is a subfield of text mining. The TextBlob model was used for sentiment analysis, which reveals the embedded meaning in the text as a result of the analysis. The sentiment analysis score indicates whether the language used in the text is positive or negative. The hypothesis that there is a relationship between the sentiment analysis score and Bitcoin price in the long and short terms was tested. Stationarity was necessary to perform an analysis on time series. Firstly, structural break and traditional unit root tests were used. The data from June 2021 to June 2022 was analyzed daily, with December 2021 being the center point for the high Bitcoin price. The data of the study are Bitcoin price and sentiment analysis scores. As a result of the study, Twitter data was found to be the dependent variable, and no long-term relationship was found with Bitcoin prices. However, a significant and positive relationship was obtained in the short term.
Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility
Virtual Economics, 2021
This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sen...