Sentiment Analysis of News for Effective Cryptocurrency Price Prediction

Vo, Anh-Dung

doi:10.18178/ijke.2019.5.2.116

Cited by 29 publications

(21 citation statements)

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“…Given that these coins are entirely dematerialized and that their value is given only by the play of supply and demand, without a Central Bank or a Government acting to protect their weight, "sentiment analysis is an essential perspective for the prediction of the price of the cryptocurrency, due to the interactive nature of financial activities" [23].…”

Section: The Usefulness Of Sentiment Analysis a Fundamental Component...mentioning

confidence: 99%

The Degree of Adoption of Business Intelligence in Romanian Companies—The Case of Sentiment Analysis as a Marketing Analytical Tool

et al. 2022

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The structural changes in the public communication space through the advent of the Internet and the further development of online commerce culminate today with the explosion of blockchain techniques and social networks. This communication space was quickly taken over by marketing tools, as demonstrated by the many marketing campaigns dedicated to these new communication channels. The development of online commerce and the emergence of social networks have allowed consumers to efficiently search for brands/products/services, compare them, express their point of view on them, and even give them grades. Due to the explosion of relevant data online, the changing business environment needs attention to interpret and extract relevant information. The application of sentiment analysis to public reaction in the online environment provides the researcher with how the authors of the analyzed texts (clients/beneficiaries) express themselves regarding the studied reference (product/service/organization/social theme and a feature of them). Along with the other metrics present in marketing, including digital marketing, the reports in the analysis panels of google analytics and social networks, sentiment analysis instantly provides the general and competitive context in which the product/service/theme evolves. In this article, two types of research have been conducted to highlight the benefits felt, but also the degree of knowledge, implementation, and use of sentiment analysis in online marketing analysis. One of the types of research was qualitative, carried out on 10 participants (specialists in the field of marketing), with the help of an interview guide. Qualitative research aims to find out the level of knowledge of sentiment analysis and the general degree of digitalization of Romanian companies, an indicator considered critical in the new post-pandemic business environment. The second research was quantitative and used to develop analysis by structural equations. For this, a questionnaire applied to a sample of 108 respondents was used. Through the analysis by structural equations, a conceptual model was developed that presents the main factors that are related to others and that contribute to the satisfaction of the users of the analysis of feelings for obtaining marketing data.

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Section: The Usefulness Of Sentiment Analysis a Fundamental Component...mentioning

confidence: 99%

The Degree of Adoption of Business Intelligence in Romanian Companies—The Case of Sentiment Analysis as a Marketing Analytical Tool

et al. 2022

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“…To understand the relationship between Bitcoin and Etherum news and the price prediction, Vo et al (2019) conducted an analysis based on the assumption that there is a relationship between the mood of the public and the cryptocurrency market, like traditional financial markets. Data were obtained using daily time series data from July 30, 2017 to October 5, 2018.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-based Cryptocurrency Price Prediction with Chaos Theory, Onchain Analysis, Sentiment Analysis and Fundamental-Technical Analysis

Akgül

Şahin

ŞENOL

2022

Chaos Theory and Applications

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Crypto assets succeeded in making their name known to large masses with Bitcoin, which emerged as a result of the creation of the first genesis block in 2008. Until 2010, the aforementioned recognition showed itself mostly in areas such as games, but over time it managed to enter the portfolios of individual investors. Especially as of end of 2017, the rapid increases in monetary value quickly attracted the attention of corporate companies and then the (Central Banks). These assets have created different alternatives (also know as altcoins) by working and have managed to become one of the important financial instruments today. This study has examined in detail the techniques (Chaos, Onchain and Sentiment) developed on the price predictions of crypto assets, which are very important in terms of the number and quality of investors. In the study, findings were obtained that new techniques such as onchain and sentiment are more prominent in estimating crypto asset prices compared to traditional asset price estimation methods of crypto assets and that these techniques can make consistent estimations.

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“…They reported to have used twenty million tweets relating to the subject, and applied different machine learning tools such as MLP, SVM, and RF, with Litecoin gaining the highest percentage of 0.8 precision score, followed by Bitcoin and Ripple then Ethereum, and the MLP was the best model providing the best results for three out of the four tested cryptocurrencies. Vo et al (2019) looked at ability of discerning Ethereum prices using the news and historical price data, while Misnik et al (2019) used neural network to predict the market price of cryptocurrencies using psychological factors and social network factors.…”

Section: Introductionmentioning

confidence: 99%

Relationship and Causality between Cryptocurrencies, Commodities, Currencies, Indexes and Web Search Results During and Prior to Covid-19 Pandemic

Memic¹,

Memic²,

Almehairi³

2021

IJBPM

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We observe the relationship and causality between cryptocurrencies on one, and commodities, currencies, equity indexes and web search results on the other side. We use prices of Bitcoin and Ethereum for cryptocurrencies, prices of crude oil and gold for commodities, Euro-US Dollar, Euro-Swiss Franc exchange rates for currencies, Dow Jones Industrial Average for market index and Google Trends® data as a measure of worldwide web search results for cryptocurrencies of interest. We find that Bitcoin and web search results correlation went from highly positive to low negative during the COVID-19 period. The results of the study show that the price of Bitcoin and Ethereum can be modelled using different combinations of commodities, currencies, indexes and web search results, with web search results and Dow Jones Industrial Average exhibiting best predictive power both concurrently and one day in advance. Our best performing models were able to explain more than 95% and 90% of Bitcoin and Ethereum price variability respectively. We also find strong evidence of web search traffic impacting both Bitcoin and Ethereum prices at all tested lags, as well as some evidence of gold impact on Bitcoin and EUR/CHF impact on Ethereum.

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Sentiment Analysis of News for Effective Cryptocurrency Price Prediction

Cited by 29 publications

References 0 publications

The Degree of Adoption of Business Intelligence in Romanian Companies—The Case of Sentiment Analysis as a Marketing Analytical Tool

The Degree of Adoption of Business Intelligence in Romanian Companies—The Case of Sentiment Analysis as a Marketing Analytical Tool

Blockchain-based Cryptocurrency Price Prediction with Chaos Theory, Onchain Analysis, Sentiment Analysis and Fundamental-Technical Analysis

Relationship and Causality between Cryptocurrencies, Commodities, Currencies, Indexes and Web Search Results During and Prior to Covid-19 Pandemic

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