Forecasting Price of Cryptocurrencies Using Tweets Sentiment Analysis

Jain, Arti; Tripathi, Shashank; DharDwivedi, Harsh; Saxena, Pranav

doi:10.1109/ic3.2018.8530659

Cited by 65 publications

(36 citation statements)

References 14 publications

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“…In the non-linear time series forecast, CNN and Seq2Seq LSTMs were successfully coupled for dynamic modeling of short-and long-term dependent patterns. The study in [37] focused on social factors, which are increasingly being utilized for online transactions throughout the world, by using a multi-linear regression model and that analyzes two big capital market cryptocurrencies, BTC and LTC. The authors of [37] found that the R2 scores were 44% for LTC and 59% for BTC.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Hamayel

Owda

2021

139

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Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.

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Section: Literature Reviewmentioning

confidence: 99%

“…The study in [37] focused on social factors, which are increasingly being utilized for online transactions throughout the world, by using a multi-linear regression model and that analyzes two big capital market cryptocurrencies, BTC and LTC. The authors of [37] found that the R2 scores were 44% for LTC and 59% for BTC. Ref.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Hamayel

Owda

2021

139

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“…Jain, Tripathi, Dwivedi, and Saxena (2018) introduced a novel method to predict the price of two of the most widely used cryptocurrencies, namely litecoin and bitcoin, based on sentiments of users' tweets. Useful features from the tweets were analyzed and extracted in a multiple linear regression model for price prediction of the cryptocurrencies using R 2 score.…”

Section: Techniques For Cryptocurrency Price Predictionmentioning

confidence: 99%

“…Later, the lowest and highest prices of cryptocurrencies were predicted using linear regression. Though the proposed approach yielded high efficiency, the experiments were limited to smaller data sets Jain, Tripathi, Dwivedi, and Saxena (2018). introduced a novel method to predict the price of two of the most widely used cryptocurrencies, namely litecoin and bitcoin, based on sentiments of users' tweets.…”

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confidence: 99%

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

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Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

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“…The users' reactions prompt many companies to decide their strategy based upon it. Jain et al [22] predict the prices of Bitcoin and Litecoin using sentiments from the users' posted tweets related to the two types of cryptocurrencies. The Multiple Linear Regression (MLR) determines the price of the cryptocurriencies with R2_score of 44% for Bitcoin and 59% for Litecoin respectively.…”

Section: Related Workmentioning

confidence: 99%

An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

Yadav¹,

Yadav

Jain

2018

ICST Transactions on Scalable Information Systems

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In today's society, sentiment analysis has gained due importance as it provides useful information about products that are used by variety of users. It gives a sneak peek of users' reactions towards the products that are available in the market at an early stage. It thus intimates users' perception and charts out a path that is beneficial for the market to grow as a whole. Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of the detailed components based products and their associated features, a novel method is desired to meet these criteria. So far, no such method is explored that analyses the product's components and their features simultaneously, on the basis of sentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis of product reviews while formulating a tree structure of product, components, and associated features. In addition, evaluation of double negative sentences, detecting questions and emotions from the review sentences are measured which increases efficiency of the FBA method. The comparison of product's components reviews is done with other existing algorithms-TF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.

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Forecasting Price of Cryptocurrencies Using Tweets Sentiment Analysis

Cited by 65 publications

References 14 publications

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

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