Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis

Pant, Dibakar Raj; Neupane, Prasanga; Poudel, Ankur; Pokhrel, Anup; Lama, Bishnu Kumar

doi:10.1109/cccs.2018.8586824

Cited by 103 publications

(76 citation statements)

References 4 publications

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“…Furthermore, inconsistencies in the reporting prohibit reproducing the empirical tests. These inconsistencies can stem from reporting to optimize the number of units in a hidden layer of a multilayer perceptron within a specific range and using a number outside of that range in the final model [47] or setting up a regression problem, but using the accuracy metric for model evaluation without further explanation [48].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Bitcoin Pricing — A Structured Literature Review

Jaquart¹,

Dann²,

Martin³

2020

WI2020 Zentrale Tracks

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Bitcoin, as the most popular cryptocurrency, has received increasing attention from both investors and researchers over recent years. One emerging branch of the research on bitcoin focuses on empirical bitcoin pricing. Machine learning methods are well suited for predictive problems, and researchers frequently apply these methods to predict bitcoin prices and returns. In this study, we analyze the existing body of literature on empirical bitcoin pricing via machine learning and structure it according to four different concepts. We show that research on this topic is highly diverse and that the results of several studies can only be compared to a limited extent. We further derive guidelines for future publications in the field to ensure a sufficient level of transparency and reproducibility.

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Section: Discussionmentioning

confidence: 99%

Machine Learning for Bitcoin Pricing — A Structured Literature Review

Jaquart¹,

Dann²,

Martin³

2020

WI2020 Zentrale Tracks

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“…Dibakar Raj Pant et al [23] They propose method prediction price of Bitcoin using RNN and combined using Sentiment analysis with good result and can see the impact of fluctuations value from negative or positive sentiments…”

Section: Overview Of Economic Value Estimation Cryptocurrency In Compmentioning

confidence: 99%

“…Python, R, Weka, and Mathlab) From Alex Greaves et al 2015 , their reseach propose prediction method using transaction graph to predict the price of bitcoin, their colled data from CS224 Website, and using feature Extraction and using SVM algorithm and Linear Regression to provide the results. Pant et al 2018 Provide method to estimation bitcoin price using Reccurent Neural Network and Sentiment analysis. Propose method that they provide is unique , The major contribution of this work is a sentiment analyserwhich can distinguish between the positive and negative tweets of Bitcoin over the Twitter with the accuracy of 81.39% shown on table 7.…”

Section: Cryptocurrency Value Back By Gold Estimation Toolsmentioning

confidence: 99%

“…It also shows a moderate correlation of 0.41between rise of negative opinions in the Twitter related toBitcoin and its consequent fall in price. From some previous research paper their produce good results and good techniques to provide Cryptocurrency estimation to help the investor to make decision of investment in cryptocurrency With various algorithm and various results, in the Arief radityo research they tried to combine Advance technique using deep learning and combine with market technical analysis , the result is more rich than other, but disadvantage their not mention the result is good or not to help investor to make decision, maybe because their using MAPE to evaluate the results, Dibakar Raj Pant et al 2018 Provide method to estimation bitcoin price using Recurrent Neural Network and Sentiment analysis It also shows a moderate correlation of 0.41between rise of negative opinions in the Twitter-related to bitcoin and its consequent fall in price. The proposed method that they provided is unique Future works will be, the direction will optimize the dataset shrouded the noise, use some feature, and make shorter training time, combine with some technique and algorithm (Hybrid methods) we can select from the previous research that provide good results, Eq.…”

Section: Cryptocurrency Value Back By Gold Estimation Toolsmentioning

confidence: 99%

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A Study of Economic Value Estimation on Cryptocurrency Value back by Gold, Methods, Techniques, and Tools

et al. 2019

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After Bitcoin Introduced around the world, many Cryptocurrencies was created that followed the standard of bitcoin. The use of Bitcoin or other Cryptocurrency as a currency is also an interesting study from an Islamic economic perspective. They tried to use gold with value back by gold , which gold itself is famous for its exchange rate stability.From abu bakar There is a need for monitoring organization of the cryptocurrency, to controlling from Riba (Interest), Maysir (gambling) and ghahar (Uncertainty). To solve this problem there is a need a tool that can predict with certainty based on valid historical data, to produce accurate prediction results and produce Economic value estimations that are close to Gold real value. With the results we can monitoring day by day, see next day value and continuously based on Cryptocurrency with value back by gold, and see what other impact influences the value by looking the factor negative or positive with sentiment analysis. In the last section we discuss and provide method that we analyse from previous work to produce method to estimate value cryptocurrency value back by gold.Ferdiansyah 1 , Siti Hajar Othman 2 , at all | 179 the first cryptocurrency has enjoyed huge success with the capital market reaching 10 billion dollars in 2016 [1].

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“…Further, [13], [15]- [17] provided evidence that the polarity of public opinion is powerful for predicting public interest in Bitcoin. Pant et al [18] added sentiment analysis of tweets to an RNN to produce 77.6% accuracy . Kim et al [16] achieved an accuracy of over 70% for Bitcoin, Ethereum and Ripple using sentiment features extracted from online forum discussions.…”

Section: Related Workmentioning

confidence: 99%

C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

Bai

White

Xiao

et al. 2019

2019 IEEE International Conference on Blockchain (Blockchain)

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We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d−1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time -rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p < 10 −5 .

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Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis

Cited by 103 publications

References 4 publications

Machine Learning for Bitcoin Pricing — A Structured Literature Review

Machine Learning for Bitcoin Pricing — A Structured Literature Review

A Study of Economic Value Estimation on Cryptocurrency Value back by Gold, Methods, Techniques, and Tools

C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine

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