Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds

Authors

  • Thanekar A Dept. of Information Technology, Shah & Anchor Kutchhi Engineering College, Mumbai University, Mumbai, India
  • Shelar S Dept. of Information Technology, Shah & Anchor Kutchhi Engineering College, Mumbai University, Mumbai, India
  • Thakare A Dept. of Information Technology, Shah & Anchor Kutchhi Engineering College, Mumbai University, Mumbai, India
  • Yadav V Dept. of Information Technology, Shah & Anchor Kutchhi Engineering College, Mumbai University, Mumbai, India

DOI:

https://doi.org/10.26438/ijcse/v7i2.148152

Keywords:

Bitcoin, Long Short Term Memory, ARIMA, Deep Learning, Sentiment Analysis

Abstract

Bitcoin has recently attracted lots of attention in various sectors like economics, computer science, and many others due to its nature of combining encryption technology and monetary units. Now-a-days social media is perfectly representing the public sentiment and opinion about Trending events. Especially, twitter has attracted a plenty of attention from analyst for studying the public sentiments. Bitcoin prediction on the basis of general public sentiments tweeted on twitter has been an intriguing field of research. This paper aims to see how well the change in Bitcoin prices, the ups and downs, is correlated with the public opinions being expressed in tweets. Understanding people’s opinion from a text tweet is the objective of sentiment analysis. Sentiment analysis and machine learning algorithms are going to be applied to the tweets which are captured from twitter and analyse the correlation between Bitcoin movements and sentiments in tweets. In an elaborate way, positive tweets in social media about a Bitcoin are expected to encourage people to invest in the crypto currency and as a result the Bitcoin price would increase.

References

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[2] Hong Kee Sul, Alan R Dennis, and Lingyao Ivy Yuan.“Trading on twitter: Using social media sentiment to predict stock returns,”, Decision Sciences, 2016.

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[4] Stuart Colianni, Stephanie Rosales, and Michael Signorotti. “Algorithmic trading of cryptocurrency based on twitter sentiment analysis,”, 2015.

[5] Hong Kee Sul, Alan R Dennis, and Lingyao Ivy Yuan.“Trading on twitter: Using social media sentiment to predict stock returns,”, Decision Sciences, 2016.

[6] Dejan Vujičić, Dijana Jagodić, Siniša Ranđić. “Blockchain Technology, Bitcoin, and Ethereum: A Brief Overview.” 2018 17th International Symposium INFOTEH-JAHORINA (INFOTEH), 2018, doi:10.1109/infoteh.2018.8345547.

[7] Jang, Huisu, and Jaewook Lee. “An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information.” IEEE Access, vol. 6, 2018, pp. 5427–5437., doi:10.1109/access.2017.2779181.

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Published

2019-02-28
CITATION
DOI: 10.26438/ijcse/v7i2.148152
Published: 2019-02-28

How to Cite

[1]
A. Thanekar, S. Shelar, A. Thakare, and V. Yadav, “Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds”, Int. J. Comp. Sci. Eng., vol. 7, no. 2, pp. 148–152, Feb. 2019.

Issue

Vol. 7 No. 2 (2019): IJCSE February Edition

Section

Research Article

License

Creative Commons License

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