Cryptocurrency – Sentiment Analysis in Social Media


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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [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 52nd Annual Meeting of the Association for Computational Linguistics, (ACL 2014), pp. 55-60.

  • [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.

  • [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.

  • [4] Ferrucci, D. (2012), Introduction to “This is Watson”, IBM Journal of Research and Development, vol. 56, pp. 1-15.

  • [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.

  • [6] Pak, A. and Paroubek, P. (2010), Twitter as a Corpus for Sentiment Analysis and Opinion Mining, Proceedings of the 7th Conference on International Language Resources and Evaluation (LREC’10), vol. 10, pp. 1320-1326.

  • [7] Dalmia, A., Gupta, M. and Varma, V. (2015), Twitter Sentiment Analysis. The good, the bad and the neutral!, Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 520-526.

  • [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.

  • [9] Kouloumpis, E., Wilson, T. and Moore, J. (2011), Twitter Sentiment Analysis: The Good, the Bad and the Omg!, Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, pp. 538-541.

  • [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.

  • [11] Colianni, S., Rosales, S. C. and Signorotti, M. (2015), Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis, CS229 Project, pp. 1-5.

  • [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.

  • [13] Stanford CoreNLP (2018) [Online]. Available:

  • [14] Stanford Sentiment Analysis (2018) [Online]. Available:

  • [15] Natural Language Understanding (2018) [Online]. Available:

  • [16] Search Tweets (2018) [Online]. Available:

  • [17] Reddit Api (2018) [Online]. Available:

  • [18] CoinMarketCap (2018) [Online]. Available:

  • [19] BitInfoCharts (2018) [Online]. Available:


Journal + Issues