A Corpus of BTC Tweets in the Era of COVID-19

Pano, Toni; Kashef, Rasha

doi:10.1109/iemtronics51293.2020.9216427

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Linear Regression, RNN, and LSTM [19], [20], [22], [23], ARIMAX [21], Linear discriminant analysis [24], ARIMA [25], Logistic regression, Naive Bayes, SVM [26], Multiple linear regression [27], Tweet corpus in COVID-19 era [28], Random Forest, Decision tree, AdaBoost [29], XGBoost-Composite model [30], Q-Learning [This paper].…”

Section: Striving For Accurate Predictionmentioning

confidence: 99%

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Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Sattarov¹,

Choi²

2022

Preprint

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Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: (i ) the number of followers of the tweet poster, (i i ) the number of comments on the tweet, (i i i ) the number of likes, and (i v ) the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80% less time, 88.8% less CPU consumption, and 12.5% more accurate predictions compared with the classic approach.

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Section: Striving For Accurate Predictionmentioning

confidence: 99%

“…al. [28] provided a corpus of tweet text for Bitcoin-related tweets during the summer of the COVID-19 period. This dataset is publicly available and considers three months to perform unimpeded research.…”

Section: B Striving For Accurate Predictionmentioning

confidence: 99%

Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Sattarov¹,

Choi²

2022

Preprint

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“…Similar with above researchers, Jain et al [27] focused on current tweets by classifying positive, negative, As Bitcoin has no central authority to control and its fluctuations are relevant to ongoing news and events, some researchers have studied how COVID-19 outbreak data (number of new cases, recovery, and deaths) can impact the future price of Bitcoin. Pano et al [28] provided a corpus of tweet text for Bitcoin-related tweets during the summer of the COVID-19 period. This dataset is publicly available and considers three months to perform unimpeded research.…”

mentioning

confidence: 99%

Twitter Attribute Classification With Q-Learning on Bitcoin Price Prediction

Sattarov

Choi

2022

IEEE Access

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Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques, and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: (i) the number of followers of the tweet poster, (ii) the number of comments on the tweet, (iii) the number of likes, and (iv) the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80% less time, 88.8% less CPU consumption, and 12.5% more accurate predictions compared with the classic approach. 16 17 processing and application in the field of predicting future 49 Bitcoin prices. Processing a large amount of Bitcoin-related 50 tweets normally consumes a high level of computer resources 51 (CPU, RAM, memory) and time [31], [32], [33], [34]. Most of 52 the previous works is focused on how to reduce the resource, 53 so maximizing the prediction result at the same time is not 54 considered. However, tweets written by an expert, public fig-55 ure, or celebrity will become viral, with many replies, likes, 56 and retweets. Tweets with few replies, likes, or retweets are 57 unlikely to become viral because they are likely to circulate 58 mainly among close friends. Consequently, viral tweets are 59 expected to have a greater influence on price changes than 60 other tweets. If we can separate tweets with the highest 61 impact on future price changes from less important tweets, 62 it gives the possibility to employ less computer resources 63 usage while still obtaining accurate forecasts. 64 Hence, different from the previous approaches, in this 65 study, we analyze how Bitcoin-related information on Twitter 66 affects the actual Bitcoin price by considering four main 67 attributes: (i) the number of followers of the poster, (ii) the 68 number of comments on a tweet, (iii) the number of likes, 69 and (iv) the number of retweets. For this, we gather all 70 Bitcoin-related tweets within a particular period and divide 71 them into four groups based on their attributes. Since we 72 use th...

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Psycholinguistic and emotion analysis of cryptocurrency discourse on X platform

Tash,

Kolesnikova,

Ahani

et al. 2024

Sci Rep

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This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our goal was to conduct a psycholinguistic and emotional analysis of social media content associated with these cryptocurrencies. Such analysis can enable researchers and experts dealing with cryptocurrencies to make more informed decisions. Our work involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns emerging in each coin’s community. To achieve this, we utilized advanced text analysis techniques. Additionally, this work unveiled an understanding of the interplay between these digital assets. By examining which coin pairs are mentioned together most frequently in the dataset, we established co-mentions among different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from X. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable perception into the linguistic nuances of various digital coins’ online communities and provides a deeper understanding of their interactions in the cryptocurrency space.

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A Corpus of BTC Tweets in the Era of COVID-19

Cited by 10 publications

References 10 publications

Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Twitter Attribute Classification With Q-Learning on Bitcoin Price Prediction

Psycholinguistic and emotion analysis of cryptocurrency discourse on X platform

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