Previsão da tendência da bitcoin utilizando extração de sentimentos do Twitter

Bitcoin is the first decentralized digital currency constituting a successful alternative economic system. As a result, the Bitcoin financial market occupies an important position in society, where it has gained increasing popularity. The correct prediction of this type of market can drastically reduce losses and maximize investor profits. One of the most popular aspects of predicting the cryptocurrency market is the analysis of sentiment in posts shared publicly on social networks. Currently, the Twitter platform generates millions of posts a day, which has attracted several researchers in search of problem solving using sentimental analysis in tweets. With this evolution, it is intended to develop, through Artificial Intelligence (AI) techniques, models capable of predicting the Bitcoin trend based on daily sentimental analysis of posts made on the Twitter platform with Bitcoin’s historical data. Specifically, it is intended to assess whether sentiment positively influences the Bitcoin trend, and whether positive, neutral and negative feelings positively influence the Bitcoin trend in the same way. Finally, it is also objective to assess whether indicators such as market volume and the volume of tweets carried out within the scope of the Bitcoin theme positively influence its trend. To validate the potential of the study, two AI models were developed. The first model was created to classify the sentiments of tweets into three typologies: positive, neutral and negative. This model focused on AI techniques based on Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BI-LSTM) and Convolutional Neural Network (CNN). In turn, the second model was designed to classify Bitcoin’s future trends into strong uptrend, uptrend, downtrend and strong downtrend. In this sense, the model focused on AI techniques based on LSTM and Random Forest Classifier. In general, it was possible to achieve good performance in the development of sentiment classification models, achieving an accuracy value of 87 % in the LSTM and BI-LSTM models and 86% in the model based on CNN technology. Regarding the model focused on predicting the Bitcoin trend, it was possible to validate that sentiment positively influences the Bitcoin trend prediction. More interestingly, neutral sentiment volume has a more significant impact on Bitcoin trend prediction. The Random Forest Classifier technique proved to be the best, recording accuracy of 57.35% in predicting the Bitcoin trend. Removing the sentiment variable made it possible to verify a cadence of 15% to 20% in the Bitcoin trend forecast, which effectively validates that sentiment positively influences the trend forecast.A Bitcoin é considerada a primeira moeda digital descentralizada constituindo um sistema económico alternativo de sucesso. Em resultado, o mercado financeiro da Bitcoin ocupa uma posição importante na sociedade, onde tem vindo a angariar cada vez mais popularidade. Prever acertadamente este tipo de mercado pode reduzir drasticamente as perdas e maximizar os lucros dos investidores. Um dos aspetos mais populares, quando se trata de prever o mercado de cryptomoedas, passa pela análise de sentimentos em posts partilhados publicamente em redes sociais. Atualmente, a plataforma do Twitter, gera milhões de posts todos os dias, o que tem atraído diversos investigadores na procura de resoluções de problemas com recurso à análise sentimental em tweets. Com esta evolução, pretende-se desenvolver através de técnicas de Inteligência Artificial (IA), modelos capazes de prever a trend da Bitcoin com base numa análise sentimental diária dos posts efetuados na plataforma do Twitter com os dados históricos da Bitcoin. Em específico, tenciona-se avaliar se o sentimento influencia positivamente a trend da Bitcoin, bem como avaliar se os sentimentos positivos, neutros e negativos, de forma isolada, influenciam da mesma forma positivamente a trend da Bitcoin. Por fim, é ainda objetivo, avaliar se indicadores como o volume de mercado e o volume de tweets realizado no âmbito do tema da Bitcoin influenciam positivamente a trend da mesma. De forma a validar o potencial do estudo, foram desenvolvidos dois modelos de IA. O primeiro modelo foi criado para efetuar a classificação de sentimentos dos tweets em três tipologias: positivos, neutros e negativos. Este modelo, focou-se em técnicas de IA basedas em LSTM, BI-LSTM e CNN. Por sua vez, o segundo modelo foi elaborado para classificar as trends futuras da Bitcoin em quatro tipologias: strong uptrend, uptrend, downtrend e strong downtrend. Neste sentido, o modelo focou-se em técnicas de IA baseadas em LSTM e Random Forest Classifier. Em geral, foi possível atingir uma boa performance no desenvolvimento dos modelos de classificação de sentimento, atingindo um valor de accuracy de 87% nos modelos LSTM e BI-LSTM, e 86% no modelo baseado na técnica de CNN. Em relação ao modelo focado em prever a trend da Bitcoin, foi possível validar que o sentimento realmente influencia positivamente a previsão da trend da Bitcoin. Mais curiosamente, verificou-se que o volume de sentimento neutro tem um impacto mais significativo na previsão da trend da Bitcoin. A técnica Random Forest Classifier demonstrou ser a melhor, registando uma accuracy de 57,35% na previsão da trend da Bitcoin. Ao remover a variável sentimento foi possível verificar uma cadência de 15% a 20% na previsão da trend da Bitcoin, o que valida efetivamente que o sentimento influencia positivamente a previsão da trend

Exploring the Relationship between Influencers’ Sentiment and Cryptocurrency Fluctuation through Microblogs

Scholars and practitioners increasingly recognize the importance of microblogs in capturing eWord of Mouth (eWoM) and their predictive power for cryptocurrency markets. This research in progress paper examines the extent to which microblog messages are related to bitcoin fluctuation. Building on information systems and finance literature, we examine the interactions between influencers’ extreme sentiment and the bitcoin fluctuation using natural language processing techniques and hypothesis testing. Our preliminary results show when influencers express extreme sentiment, in favour or against bitcoin, it is less likely that their tweets are related to future bitcoin fluctuation. However, when their extreme tweets are in-depth and unique, this negative relationship is moderated. Overall, our findings reveal that influencers’ sentiment is an important predictor in determining bitcoin fluctuation, but not all tweets are of equal impact. This study offers new insights into social media and its role in the cryptocurrency market

Leveraging Twitter data to understand the dynamics of social media interactions on cryptocurrencies

Rapid technological change in the last decades has led to the emergence of new platforms and fields such as cryptocurrencies and social media data. Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide. The social media platform has millions of users where tweets are continuously shared every second. Therefore, tweets are useful when a large amount of data is generated to conduct a social media analysis. In addition, Twitter is broadly utilized by investors and financial analysts to gather valuable information. Several studies have shown that the content posted on Twitter can predict the movement of cryptocurrency prices. However, limited research has been conducted on the dynamics of Twitter interactions on cryptocurrencies among users. By leveraging 1724328 tweets, this research aims to understand the dynamics of social media users’ interactions on cryptocurrencies. Essentially by shedding light on larger cryptocurrencies contrary to smaller. The findings reveal that Twitter users are more positive than negative about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The thesis contributes to demonstrating the effectiveness of the Social set analysis framework to analyze and visualize a massive amount of social media data and user-generated data created on social media platforms such as Twitter

Islamic view towards Bitcoin

This paper proposes to analyze the agent behavior by means of big data extracted from the search engine « Google trends » and Twitter API to visualize the emotions and the manner of thinking about « Bitcoin » in the Islamic context. Two kinds of sentiment measures are constructed. The first is based on the search query of the word « Bitcoin » with religious connotation all over the world from 14/04/2017 to 14/04/2018 in weekly frequency. The second is built on twitter data from 03/04/2018 to 13/04/2018, by using a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations. In the next step, the Granger causality analysis is used to investigate the hypothesis that this sentiment causes the volatility and the returns of « Bitcoin ». The results show that, at a first-level that twitter users of the word « Islamic Bitcoin » improve positive sentiment. Secondly, the Twitter sentiment measure has a significant effect on lagged Bitcoin returns and volatility. Furthermore, this sentimental variable Granger causes Bitcoin returns and volatility.  This study contributes to the literature by studying the influence of the doctrinal view towards Bitcoin on his prices dynamics. Knowing that Bitcoin is a new financial asset and there is a large debate on his compliance with sharia

Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.Comment: Submitted to a journa

Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment

The purpose of this research is to investigate the impact of social media sentiments on predicting the Bitcoin price using machine learning models, with a focus on integrating on-chain data and employing a Multi Modal Fusion Model. For conducting the experiments, the crypto market data, on-chain data, and corresponding social media data (Twitter) has been collected from 2014 to 2022 containing over 2000 samples. We trained various models over historical data including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting and a Multi Modal Fusion. Next, we added Twitter sentiment data to the models, using the Twitter-roBERTa and VADAR models to analyse the sentiments expressed in social media about Bitcoin. We then compared the performance of these models with and without the Twitter sentiment data and found that the inclusion of sentiment feature resulted in consistently better performance, with Twitter-RoBERTa-based sentiment giving an average F1 scores of 0.79. The best performing model was an optimised Multi Modal Fusion classifier using Twitter-RoBERTa based sentiment, producing an F1 score of 0.85. This study represents a significant contribution to the field of financial forecasting by demonstrating the potential of social media sentiment analysis, on-chain data integration, and the application of a Multi Modal Fusion model to improve the accuracy and robustness of machine learning models for predicting market trends, providing a valuable tool for investors, brokers, and traders seeking to make informed decisions

Kako su aktivnosti na Twitteru povezane s ponašanjem najpoznatijih kriptovaluta? Dokazi iz analize društvenih mreža i analize sentimenta

Cryptocurrencies have embraced Twitter as a major channel of communication. Employing social network analysis and sentiment analysis, this study investigates the Twitter-mediated communication behaviors among cryptocurrencies. This study determines whether a significant association exists between cryptocurrencies\u27 Twitter networks and their credit scores. Data were drawn from the Twitter pages of several top cryptocurrencies. The results indicate that reply–mention networks had the densest structure, that the following–follower network structure was correlated with the reply–mention structure, and that the reply–mention and co–tweet networks were positively correlated. The results also indicate that cryptocurrencies\u27 active networking strategies affected their credit scores and more importantly, that cryptocurrencies frequently linked with fellow currencies tended to have high credit scores.Kriptovalute su prigrlile Twitter kao glavni kanal komunikacije kojim prenose novosti i grade odnose s (potencijalnim) ulagačima i kupcima. Služeći se analizom društvenih mreža i analizom sentimenta, rad istražuje Twitterom posredovano komunikacijsko ponašanje kriptovaluta proučavanjem učestalosti tvitova te njihovih struktura: following-follower, reply-mention i co-tweet. Ocjene tržišta često znatno utječu i na proizvođače (tj. programere) i na potrošače (tj. vlasnike kriptovaluta). Stoga ovo istraživanje utvrđuje postoji li povezanost između Twitterovih mreža kriptovaluta i njihovih kreditnih ocjena. Podaci su prikupljeni na Twitterovim stranicama niza najpoznatijih kriptovaluta. Rezultati pokazuju da su reply-mention mreže imale najgušću strukturu, da je mrežna struktura following-follower povezana sa strukturom reply-mention i da su reply-mention i co-tweet mreže pozitivno povezane. Rezultati također upućuju na to da su aktivne mrežne strategije kriptovaluta utjecale na njihove kreditne ocjene i, što je još važnije, da kriptovalute koje se češće povezuju sa srodnim valutama obično imaju visoke kreditne ocjene

Influencers, are they responsible for Bitcoin's volatility? Transfer entropy and Granger causality in prol of an answer

Bitcoin, like any other cryptocurrency, is subject to fluctuations in price. The volatility of this market can be a reflection of several reasons, such as public opinion, social networks and news. Social networks, in particular Twitter, are increasingly used as an important source of value extraction because through this network, it is possible to find out about news in real-time, follow the repercussions, know what experts in the financial world are commenting or thinking and even decide based on influencer's opinion whether to invest or not. This study investigates the influence that a specific group of people exert on Bitcoin volatility. A selection of influencers from the “crypto world” was made, and through the Twitter API, it was possible to select the tweets of the object of study. To choose the classification model for sentiment analysis, two techniques were compared, one being very popular with a focus on the domain of social networks and the other recently created and focused on finance. From the selected technique, only positive and negative sentiments were considered, and then the daily series of the Sentiment Score was calculated. Next, the causal relationship between Bitcoin and sentiment was investigated using Granger causality and Transfer Entropy tests. Transfer Entropy showed encouraging results, suggesting that there is a transfer of information from Sentiment to Returns and that it is possible for an influencer to contribute to Bitcoin’s volatilityO Bitcoin, assim como qualquer outra criptomoeda, está sujeito a flutuações no preço. A volatilidade desse mercado pode ser reflexo de vários motivos, tais como, opinião pública, redes sociais e notícias. As redes sociais, em particular o Twitter, cada vez mais é utilizado como uma fonte importante de extração de valor, isto porque através desta rede é possível saber das novidades em tempo real, acompanhar as repercussões, saber o que entendedores do mundo financeiro estão a comentar e decidir até mesmo com base na opinião de um influenciador se irá investir ou não. Este estudo investiga a influência que determinadas pessoas exercem sobre a volatilidade do Bitcoin. Foi feita uma seleção de influenciadores do “mundo crypto” e através da API do Twitter foi possível selecionar os tweets de objeto de estudo. Para a escolha do modelo de classificação para análise de sentimento foram comparadas duas técnicas, sendo uma muito popular com foco no domínio de redes sociais e a outra recém-criada e focada em finanças. A partir da técnica selecionada, apenas os sentimentos positivos e negativos foram considerados e então calculada a série diária do Sentiment Score. A seguir foi investigada a relação causal entre o Bitcoin e o sentiment utilizando os testes de causalidade de Granger e Entropia de Transferência. A Entropia de Transferência mostrou resultados animadores que sugerem existir transferência de informação de Sentiment para Returns e que, portanto, é possível que um influencer contribua para a volatilidade do Bitcoin

Public Perceptions of Facebook’s Libra Digital Currency Initiative: Text Mining on Twitter

Large corporations in the financial and technology sectors are increasingly interested in digital currencies, and central bank digital currencies are being actively researched around the globe. In this study, we analyzed the public discourse conducted through the social media platform Twitter concerning Facebook’s Libra digital currency initiative. Text mining of tweets posted during the one-month period around the official announcement of the digital currency project revealed that the majority of the public have a neutral sentiment toward the proposed digital currency. However, those with positive attitudes outnumbered those perceiving the digital currency initiative as negative, and the negative sentiment mainly stemmed from anger and anxiety. Through topical modeling analysis using latent Dirichlet allocation, we identified eight themes in the public discourse related to Facebook Libra. The study provides an early exploratory assessment of factors facilitating and hindering user adoption of one of the most important practical applications of blockchain technology