In recent years, cryptocurrencies have become more and more popular around the world, and they are being accepted and used by more countries. Cryptocurrencies are decentralized, and they form an emerging market that is different from stocks. At present, there is already much work around the stock price prediction using news articles, but there are few papers on the cryptocurrency market. In this paper, we aim to research the effects of news articles on bitcoin prices. We extract features from news articles with both commonly used text feature extraction algorithms (e.g., N-Gram and TF-IDF) and SentiGraph, which is a novel text representation method we propose. SentiGraph takes advantages of sentiment analysis and transforms a news article into a graph. Compared with previous feature extraction methods, our experiment results show that this new approach is superior on the prediction accuracy, which also demonstrates the impacts of news articles on the bitcoin price.
We investigate the extent to which Bitcoin price fluctuations are associated with investors’ sentiment disagreement. We employ three textual sentiment analysis techniques: 1) a Python library offered by the Computational Linguistics and Psycholinguistics Research Center; 2) Loughran and McDonald’s (2011) dictionary; and 3) semantic orientation by the point-wise mutual information method. The results show that investors’ attention and sentiment disagreement induce extremely high volatility and jumps in Bitcoin prices. These findings complement existing studies on how investors’ sentiment manifests in asset prices.
The Bitcoin protocol and its underlying cryptocurrency have started to shape the way we view digital currency, and opened up a large list of new and interesting challenges. Amongst them, we focus on the question of how is the price of digital currencies affected, which is a natural question especially when considering the price rollercoaster we witnessed for bitcoin in 2017-2018. We work under the hypothesis that price is affected by the web footprint of influential people, we refer to them as crypto-influencers.
Abid Inamdar, Aarti Bhagtani, Suraj K. Bhatt, Pooja M. Shetty
This paper cross validates thesis given by few authors on the impact of social media on cryptocurrency prices. Initially, the focus is on the Bitcoin, later on, a similar model can be used for other cryptocurrencies. Sentiment scores of tweets and news feeds are considered along with historical prices and its volume to predict prices. Experimental results show that there is not much impact of sentiment scores unless these scores are not biased to one particular class.
Sosyal medya, insanları alış veriş alışkanlıklarından yatırım kararlarına kadar birçok ticari niyetleri üzerinde yüksek etki düzeyi olduğu güncel birçok çalışmada araştırılmaya başlanmıştır ve bu ilişki ortaya konmuştur. Bu ilişki üzerine inşa edilerek geliştirilen güncel analiz yöntemleri yatırım araçlarının gelecek değerlerini tahmin ederek yatırım kararları almada bir destek mekanizması olarak kullanılması çok cazip bir konudur. Bu sebeple bu ilişki yatırımcı ve analistlerden akademisyenlere kadar güncel bir ilgi konusu olmuştur. Bu çalışmanın amacı da sosyal medya ile yatırım kararları arasındaki ilişkiyi metinsel ve finansal analiz aracılığı ile görmeye çalışmaktır. Bu çalışmada Twitter üzerinden metin madenciliği ile veri çekilmiş ve sentiment(duygu) analizi ile yorumların olumlu ya da olumsuz olma durumu incelenmiştir. Sentiment analizinden elde edilen sayısal değerler ile güncel ve küresel bir yatırım aracı olan Bitcoin fiyatları arasındaki ilişkinin varlığını sorgulamak adına Granger Nedensellik analizi gibi finansal analizler kullanılmıştır.
International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Roy Bar-Haim, Dalia Krieger, Orith Toledo‐Ronen, Lilach Edelstein · 10 authors
Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
Cryptocurrencies, like Bitcoin, have become increasingly popular over the last decade. The price of Bitcoin has gone through several cycles of highs and lows. As a result, it is a widely discussed topic, especially on platforms like Twitter. Sentiment analysis is a research area of Natural Language Processing. It is used to determine whether the text is positive, negative, or neutral. Twitter tweets are more challenging to analyze when compared to other forms of text, due to the presence of irregular grammar, emoticons, and sarcasm. This study intends to analyze the effect of tweets on the stock price of Bitcoin. In order to study the effect, the sentiment associated with each tweet is calculated using VADER, and also the profession and follower countassociated with verified users who tweet about bitcoin is found. Following this, a model is trained and tested using a combined dataset of tweet related data and historical bitcoin price data. It was found that the sentiment of tweets does correlate with the shift in the price of bitcoin.
The cryptocurrency is attracting more and more attention because of the blockchain technology. Ethereum is gaining a significant popularity in blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contracts and decentralized applications (Dapps). There are many kinds of cryptocurrency information on the social network. The risks and fraud problems behind it have pushed many countries including the United States, South Korea, and China to make warnings and set up corresponding regulations. However, the security of Ethereum smart contracts has not gained much attention. Through the Deep Learning approach, we propose a method of sentiment analysis for Ethereum's community comments. In this research, we first collected the users' cryptocurrency comments from the social network and then fed to our LSTM + CNN model for training. Then we made prediction through sentiment analysis. With our research result, we have demonstrated that both the precision and the recall of sentiment analysis can achieve 0.80+. More importantly, we deploy our sentiment analysis1 on RatingToken and Coin Master (mobile application of Cheetah Mobile Blockchain Security Center23). We can effectively provide detail information to resolve the risks of being fake and fraud problems.
Online portals include an increasing amount of user feedback in form of ratings and reviews. Recent research highlighted the importance of this feedback and confirmed that positive feedback improves product sales figures and thus its success. However, online portals' operators act as central authorities throughout the overall review process. In the worst case, operators can exclude users from submitting reviews, modify existing reviews, and introduce fake reviews by fictional consumers. This paper presents ReviewChain, a decentralized review approach. Our approach avoids central authorities by using blockchain technologies, decentralized apps and storage. Thereby, we enable users to submit and retrieve untampered reviews. We highlight the implementation challenges encountered when realizing our approach on the public Ethereum blockchain. For each implementation challange, we discuss possible design alternatives and their trade-offs regarding costs, security, and trustworthiness. Finally, we analyze which design decision should be chosen to support specific trade-offs and present resulting combinations of decentralized blockchain technologies, also with conventional centralized technologies.
Youngbin Kim, Jurim Lee, Nuri Park, Jaegul Choo · 6 authors
Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.
The block size debate has been a contentious issue in the Bitcoin com-munity on the social media platform Reddit. Many members of the com-munity suspect there have been organized attempts to manipulate the debate from people using multiple accounts to over-represent and mis-represent important issues on the debate. The following analysis uses techniques from authorship attribution and machine learning to deter-mine whether comments from user accounts that are active in the debate are from the same author. The techniques used are able to recall over 90 % of all instances of multiple account use and achieve up to 72 % for the true positive rate. 1
Martina Matta, Maria Ilaria Lunesu, Michele Marchesi
In the last decade, Web 2.0 services such as blogs, tweets, forums, chats, email etc. have been widely used as communication media, with very good results. Sharing knowledge is an important part of learning and enhancing skills. Furthermore, emotions may affect decisionmaking and individual behavior. Bitcoin, a decentralized electronic currency system, represents a radical change in financial systems, attracting a large number of users and a lot of media attention. In this work, we investigated if the spread of the Bitcoin’s price is related to the volumes of tweets or Web Search media results. We compared trends of price with Google Trends data, volume of tweets and particularly with those that express a positive sentiment. We found significant cross correlation values, especially between Bitcoin price and Google Trends data, arguing our initial idea based on studies about trends in stock and goods market.