KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

Mohapatra, Shubhankar; Ahmed, Nauman; Alencar, Paulo

doi:10.48550/arxiv.2003.04967

Cited by 1 publication

(3 citation statements)

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“…However, for the purposes of this study, only positive and negative polarity scores are included in the training and evaluation data sets. VADER was widely used in related work (Valencia et al 2019;Abraham et al 2018;Kraaijeveld and De Smedt 2020;Mohapatra et al 2020;Serafini et al 2020) and provides advantages including the following: it is open source and free; it is human validated and tuned for Twitter content (Valencia et al 2019); and it has also been shown to perform competitively with human annotators and has outperformed several benchmarks, especially on social media content (Hutto and Gilbert 2015). 11 and the provided UNIX timestamps were changed to UTC format (so as to match the Twitter dataset).…”

Section: Determining Polarity Scoresmentioning

confidence: 99%

“…There is no doubt that sentiment affects an asset's price-and as put by Baker and Wurgler: "the question is no longer, as it was a few decades ago, whether investor sentiment affects stock prices, but rather how to measure investor sentiment and quantify its effects" (Baker and Wurgler 2007). Whilst this statement is referring to a well-founded body of literature on applying sentiment analysis to traditional markets (Gunter et al 2014;Rao and Srivastava 2012;Li et al 2014;Mittal and Goel 2012), sentiment analysis can similarly be used for cryptocurrency price prediction as demonstrated extensively in recent work (Valencia et al 2019;Kraaijeveld and De Smedt 2020;Abraham et al 2018;Stenqvist and Lönnö 2017;Pant 2018;Galeshchuk et al 2018;Kilimci 2020;Naeem et al 2020;Serafini et al 2020;Wołk 2019;Balfagih and Keselj 2019;Mohapatra et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Twitter 3 is widely-used as a source of sentiment data, and has become a popular social media platform amongst crypto-communities (Kraaijeveld and De Smedt 2020;Abraham et al 2018;Stenqvist and Lönnö 2017;Pant 2018;Galeshchuk et al 2018;Kilimci 2020;Naeem et al 2020;Balfagih and Keselj 2019;Mohapatra et al 2020). Whilst the current state-of-the-art has achieved encouraging results, yet further research effort is required to overcome a number of issues.…”

Section: Introductionmentioning

confidence: 99%

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Bitcoin price change and trend prediction through twitter sentiment and data volume

2022

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Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease. Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease. In this paper, we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease. We utilise not only sentiment extracted from tweets, but also the volume of tweets. We present results from experiments exploring the relation between sentiment and future price at different temporal granularities, with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change. Two different neural network models are explored and evaluated, one based on recurrent nets and one based on convolutional networks. An additional model is presented to predict the magnitude of change, which is framed as a multi-class classification problem. It is shown that this model yields more reliable predictions when used alongside a price trend prediction model. The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy ( 63%).

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