Cryptocurrency – Sentiment Analysis in Social Media

Tudor-Mircea Dulău; Mircea Dulău

doi:10.2478/amset-2019-0009

.blurhash-client-img { display: none !important; }

Cryptocurrency – Sentiment Analysis in Social Media

Acta Marisiensis. Seria Technologica

Volume 16 (2019): Issue 2 (December 2019)

By: Tudor-Mircea Dulău and Mircea Dulău

Open Access

|Dec 2019

Abstract

The paper proposes the exploration, identification and development of a Java solution for extracting the sentiment related to the cryptocurrencies phenomenon, from the content of the posts of certain popular social networks.

Detecting the positive, neutral or negative character of the sentiment is adopted as a relevant method of establishing the nature of the human perception on the topical issue defined by cryptocurrencies.

References

[1] Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. and McClosky, D. (2014), The Stanford CoreNLP Natural Language Processing Toolkit, Proceedings of the 52^nd Annual Meeting of the Association for Computational Linguistics, (ACL 2014), pp. 55-60.10.3115/v1/P14-5010
Search in Google Scholar Back to article
[2] Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A. and Potts, C. (2013), Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), vol. 1631, pp. 1631-1642.
Search in Google Scholar Back to article
[3] High, R. (2012), The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works, Redbooks and IBM Corporation, vol. 1.
Search in Google Scholar Back to article
[4] Ferrucci, D. (2012), Introduction to “This is Watson”, IBM Journal of Research and Development, vol. 56, pp. 1-15.10.1147/JRD.2012.2184356
Search in Google Scholar Back to article
[5] Kushwanth, R., Sachin, A., Shambhavi, B. and Shobha, G. (2014), Sentiment Analysis of Twitter Data, International Journal of Advanced Research in Computer Engineering and Technology (IJARCET 2014), vol. 3, no. 12, pp. 4337-4342.
Search in Google Scholar Back to article
[6] Pak, A. and Paroubek, P. (2010), Twitter as a Corpus for Sentiment Analysis and Opinion Mining, Proceedings of the 7^th Conference on International Language Resources and Evaluation (LREC’10), vol. 10, pp. 1320-1326.
Search in Google Scholar Back to article
[7] Dalmia, A., Gupta, M. and Varma, V. (2015), Twitter Sentiment Analysis. The good, the bad and the neutral!, Proceedings of the 9^th International Workshop on Semantic Evaluation (SemEval 2015), pp. 520-526.10.18653/v1/S15-2087
Search in Google Scholar Back to article
[8] Agarwal, A., Xie, B., Vovsha, I., Rambow, O. and Passonneau, R. (2011), Sentiment Analysis of Twitter Data, Proceedings of the Workshop on Languages in Social Media (LSM’11), pp. 30-38.
Search in Google Scholar Back to article
[9] Kouloumpis, E., Wilson, T. and Moore, J. (2011), Twitter Sentiment Analysis: The Good, the Bad and the Omg!, Proceedings of the 5^th International AAAI Conference on Weblogs and Social Media, pp. 538-541.10.1609/icwsm.v5i1.14185
Search in Google Scholar Back to article
[10] Younggue, B. and Hongchul, L. (2012), Sentiment Analysis of Twitter Audiences: Measuring the positive or negative in influence of popular twitterers, Journal of the American Society for Information Science and Technology, vol. 63, no. 12, pp. 2521-2535.10.1002/asi.22768
Search in Google Scholar Back to article
[11] Colianni, S., Rosales, S. C. and Signorotti, M. (2015), Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis, CS229 Project, pp. 1-5.
Search in Google Scholar Back to article
[12] Kim, Y. B., Kim, J. G., Kim, W., Im, J. H., Kim, T. H., Kang, S. J. and Kim, C. H. (2016), Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies, PLoS One, 11(8), e0161197.10.1371/journal.pone.0161197498863927533113
Search in Google Scholar Back to article
[13] Stanford CoreNLP (2018) [Online]. Available: https://stanfordnlp.github.io/CoreNLP/
Search in Google Scholar Back to article
[14] Stanford Sentiment Analysis (2018) [Online]. Available: https://nlp.stanford.edu/sentiment/
Search in Google Scholar Back to article
[15] Natural Language Understanding (2018) [Online]. Available: https://www.ibm.com/watson/developercloud/natural-language-understanding/api/v1/
Search in Google Scholar Back to article
[16] Search Tweets (2018) [Online]. Available: https://developer.twitter.com/en/docs/tweets/search
Search in Google Scholar Back to article
[17] Reddit Api (2018) [Online]. Available: https://www.reddit.com/dev/api
Search in Google Scholar Back to article
[18] CoinMarketCap (2018) [Online]. Available: https://coinmarketcap.com
Search in Google Scholar Back to article
[19] BitInfoCharts (2018) [Online]. Available: https://bitinfocharts.com
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/amset-2019-0009 | Journal eISSN: 2668-4217

Journal RSS Feed

Language: English

Page range: 1 - 6

Published on: Dec 21, 2019

Published by: University of Medicine, Pharmacy, Science and Technology of Targu Mures

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

Related subjects:

Computer sciences, other,

Engineering,

Electrical engineering,

Fundamentals of electrical engineering,

Mechanical engineering,

Fundamentals of mechanical engineering,

Mathematics,

General mathematics

© 2019 Tudor-Mircea Dulău, Mircea Dulău, published by University of Medicine, Pharmacy, Science and Technology of Targu Mures
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 16 (2019): Issue 2 (December 2019)