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Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds

Atharva Thanekar1 , Sanket Shelar2 , Aditya Thakare3 , Vivek Yadav4

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 148-152, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.148152

Online published on Feb 28, 2019

Copyright © Atharva Thanekar, Sanket Shelar, Aditya Thakare, Vivek Yadav . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Atharva Thanekar, Sanket Shelar, Aditya Thakare, Vivek Yadav, “Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.148-152, 2019.

MLA Style Citation: Atharva Thanekar, Sanket Shelar, Aditya Thakare, Vivek Yadav "Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds." International Journal of Computer Sciences and Engineering 7.2 (2019): 148-152.

APA Style Citation: Atharva Thanekar, Sanket Shelar, Aditya Thakare, Vivek Yadav, (2019). Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds. International Journal of Computer Sciences and Engineering, 7(2), 148-152.

BibTex Style Citation:
@article{Thanekar_2019,
author = {Atharva Thanekar, Sanket Shelar, Aditya Thakare, Vivek Yadav},
title = {Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2019},
volume = {7},
Issue = {2},
month = {2},
year = {2019},
issn = {2347-2693},
pages = {148-152},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3635},
doi = {https://doi.org/10.26438/ijcse/v7i2.148152}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i2.148152}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3635
TI - Bitcoin Movement Prediction Using Sentimental Analysis of Twitter Feeds
T2 - International Journal of Computer Sciences and Engineering
AU - Atharva Thanekar, Sanket Shelar, Aditya Thakare, Vivek Yadav
PY - 2019
DA - 2019/02/28
PB - IJCSE, Indore, INDIA
SP - 148-152
IS - 2
VL - 7
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

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

References

[1] Mai, Feng and Bai, Qing and Shan, Zhe and Wang, Xin (Shane) and Chiang, Roger H.L., “From Bitcoin to Big Coin: The Impacts of Social Media on Bitcoin Performance,” (January 6, 2015).
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[3] Garcia D, Tessone CJ, Mavrodiev P, Perony N. “The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy,”, 2014.
[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.