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Twitter sentiment analysis for price and transaction volume changes in the cryptocurrency market [chapter]

B.D.P. See, M. Ulpah
2022 Contemporary Research on Management and Business  
This article examines the extent to which Twitter sentiments can be used to predict price and transaction volume changes for the nine largest cryptocurrencies for the period of June 2021 to September 2021  ...  Past studies have shown that sentiment analysis may work for several cryptocurrencies, while this study only found predictive capabilities in transaction volume changes and not in price movement.  ...  As seen in Table 2 , all sentiments were stationary. Next, the Johansen Cointegration test was done using the time-series of daily sentiments, price changes, and transaction volume changes.  ... 
doi:10.1201/9781003295952-11 fatcat:4kwfndylibeixkzqiv4wfqynti

LSTM Based Sentiment Analysis for Cryptocurrency Prediction [article]

Xin Huang, Wenbin Zhang, Xuejiao Tang, Mingli Zhang, Jayachander Surbiryala, Vasileios Iosifidis, Zhen Liu, Ji Zhang
2021 arXiv   pre-print
the historical cryptocurrency price movement to predict the price trend for future time frames.  ...  This research is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them.  ...  was used to model the sentiment information and make real time prediction for the price trend.  ... 
arXiv:2103.14804v4 fatcat:f6i5bx6o25ewfl6reuhrfsdbiu

Prediction of dogecoin price using deep learning and social media trends

Basant Agarwal, Priyanka Harjule, Lakshit Chouhan, Upkar Saraswat, Himanshu Airan, Parth Agarwal
2021 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems  
Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency.  ...  The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency.  ...  from market data and social media trends for Dogecoin and uses them for price prediction.  ... 
doi:10.4108/eai.29-9-2021.171188 fatcat:krsxoc5zkfg3bjw34ztfyxwhcu

KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments [article]

Shubhankar Mohapatra, Nauman Ahmed, Paulo Alencar
2020 arXiv   pre-print
In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments.  ...  However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments  ...  However, tweets need to be filtered and their sentiments need to be calculated in a timely fashion to help predict cryptocurrency prices in real time.  ... 
arXiv:2003.04967v1 fatcat:fz54v644bjhzdfvzolt7k4awmy

The Effect of Tweets Made by Cryptocurrency Opinion Leaders on Bitcoin Prices

Shakirullah Hamza
2020 Saudi Journal of Economics and Finance  
This paper uses sentiment analysis on tweets made be cryptocurrency influencers to see whether they can be used to predict Bitcoin price fluctuations.  ...  Rapid technological advancements in the last few decades have given rise to various new products and fields, such as cryptocurrencies, social media and sentiment analysis.  ...  used cross-sectional, univariate and multivariate time series models.  ... 
doi:10.36348/sjef.2020.v04i12.005 fatcat:ejsyn2p6ifeb3f4rwisesa35hy

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

Franco Valencia, Alfonso Gómez-Espinosa, Benjamín Valdés-Aguirre
2019 Entropy  
The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and  ...  In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market  ...  Data Availability: Data used for this article is publicly available either though the corresponding Application Programming Interface or it is available alongside the required scripts to reproduce these  ... 
doi:10.3390/e21060589 pmid:33267303 fatcat:ethwomkmujg6nm6477a5c2harq

Down with the #Dogefather: Evidence of a Cryptocurrency Responding in Real Time to a Crypto-Tastemaker

Michael Cary
2021 Journal of Theoretical and Applied Electronic Commerce Research  
Recent research in cryptocurrencies has considered the effects of the behavior of individuals on the price of cryptocurrencies through actions such as social media usage.  ...  By performing sentiment analysis on relevant tweets during the time he was hosting SNL, evidence is found that negative perceptions of Musk's performance led to a decline in the price of Dogecoin, which  ...  Tweet, e.g., the impact of a president's tweets on Bitcoin [10] , predicting the price of a cryptocurrency using social media data [11, 12] , and predicting bubbles in cryptocurrency markets with social  ... 
doi:10.3390/jtaer16060123 fatcat:27rd2cwfvjgjdgvwdckhpdde2u

The Predictive Power of a Twitter User's Profile on Cryptocurrency Popularity

Maria Trigka, Andreas Kanavos, Elias Dritsas, Gerasimos Vonitsanos, Phivos Mylonas
2022 Big Data and Cognitive Computing  
Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting  ...  Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period.  ...  Let us consider the variables CP t , S t , F t , R t , FV t , and L t that represent the popularity, sentiment, followers, retweets, favorites, and lists time series data, respectively.  ... 
doi:10.3390/bdcc6020059 fatcat:fzpjnjov4zcwhkixxn2klatyuy

Deep Learning-based Cryptocurrency Price Prediction Scheme with Inter-dependent Relations

Sudeep Tanwar, Nisarg P. Patel, Smit N. Patel, Jil R. Patel, Gulshan Sharma, I. E. Davidson
2021 IEEE Access  
accuracy The Window length is relatively large [44] 2021 LSTM based sentimental analysis on social media data to predict cryptocurrency prices LSTM sentimental analyzer preci- sion = 87% recall  ...  [65] used various time series analysis techniques, such as ARIMA, with different ML and DL algorithms to predict the price of Bitcoin. Anupriya et al.  ... 
doi:10.1109/access.2021.3117848 fatcat:xzwoluapozfgfj4uecry534djm

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Ross C. Phillips, Denise Gorse
2018 Proceedings of the 4th International Conference on Frontiers of Educational Technologies - ICFET '18  
Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly.  ...  A Hawkes model is then applied to find interactions between topics and cryptocurrency prices.  ...  Cryptocurrency prediction via online data sources The use of online information, including social media, to predict financial asset movements has generated widespread interest.  ... 
doi:10.1145/3233347.3233370 fatcat:ovki35edprgyllse6q4hsmj6nq

A Novel Cryptocurrency Prediction Method Using Optimum CNN

Atif Naseer, Enrique Nava Baro, Sultan Daud Khan, Yolanda Vila, Jennifer Doyle
2022 Computers Materials & Continua  
This paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction; this method is used to predict the prices of four cryptocurrencies,  ...  cryptocurrency movements and Tweet sentiments.  ...  As NN perform better than the state-of-the-art machine learning techniques in the prediction of time-series data, CNN was used for prediction.  ... 
doi:10.32604/cmc.2022.020823 fatcat:6xagwkow7fghrmonji5zzgibk4

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics [article]

Ross C. Phillips, Denise Gorse
2018 arXiv   pre-print
Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly.  ...  A Hawkes model is then applied to find interactions between topics and cryptocurrency prices.  ...  Cryptocurrency prediction via online data sources The use of online information, including social media, to predict financial asset movements has generated widespread interest.  ... 
arXiv:1806.11093v1 fatcat:7n2ejmztu5dz5kci6kvaqxn4ke

Predictive analysis of Bitcoin price considering social sentiments [article]

Pratikkumar Prajapati
2020 arXiv   pre-print
We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin.  ...  We focus on using social sentiment as a feature to predict future Bitcoin value, and in particular, consider Google News and Reddit posts.  ...  Acknowledgments The author thanks Volkmar Frinken, Guha Jayachandran, and Shriphani Palakodety for lectures and guidance during the Blockchain Technologies course in Fall 2019.  ... 
arXiv:2001.10343v1 fatcat:v3ekxcybqzb4re7bacg564rm7u

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

Saeed Nosratabadi, Amir Mosavi, Puhong Duan, Pedram Ghamisi, Ferdinand Filip, Shahab S. Band, Uwe Reuter, Joao Gama, Amir H. Gandomi
2020 Zenodo  
Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency.  ...  The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models.  ...  Series Cryptocurrencies Price prediction Altana et al. [89] LSTM-EWT Financial Time Series Cryptocurrencies Price prediction Jiang and Liang [90] CNN Financial Time Series Cryptocurrencies  ... 
doi:10.5281/zenodo.4087812 fatcat:4flgeabkxvgjrpbydfby3v6tua

The Butterfly "Affect": impact of development practices on cryptocurrency prices

Silvia Bartolucci, Giuseppe Destefanis, Marco Ortu, Nicola Uras, Michele Marchesi, Roberto Tonelli
2020 EPJ Data Science  
We measure sentiment and emotions (joy, love, anger, etc.) of the developers' comments over time, and test the corresponding time series (i.e. the affect time series) for correlations and causality with  ...  Moreover, using an artificial recurrent neural network (LSTM), we can show that the Root Mean Square Error (RMSE)-associated with the prediction of the prices of cryptocurrencies-significantly decreases  ...  "Social" sentiment is extracted using data gathered from users' online communities [16] -e.g. online forums such as BitcoinTalk b -and from online news and tweets [17] [18] [19] .  ... 
doi:10.1140/epjds/s13688-020-00239-6 fatcat:k33lygpp6jg5pohr6igkkiizaq
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