PREDIKCIJA VREDNOSTI KRIPTOVALUTA ANALIZOM ISTORIJSKIH CENA, BLOKČEJN INFORMACIJA I SENTIMENTA TVITOVA
Apstrakt
Ovaj rad bavi se problemom predikcije cene Bitkoina na osnovu istorijskih cena i blokčejn informacija. Cenu kriptovaluta velikim delom diktiraju špekulacije pa je ispitano u kojoj meri sentiment analiza tvitova doprinosi padu ili rastu cena. Pored podataka o ceni i sentimentu, korišćeni su blokčejn podaci i istorijski podaci za još tri popularne kriptovalute: Lajtkoin, Etereum i Ripl. Za sentiment analizu ispitane su tri tehnike: konvolucione neuronske mreže, ansambli leksikona i metoda zasnovana na jezičkim pravilima. Rezultati sentiment analize upotrebljeni su u daljem procesu predviđanja kriptovaluta. Izabrani model za predikciju je rekurentna neuronska mreža sa GRU ćelijama. U fazi evaluacije posmatrala se tačnost predviđanja kretanja cene, odnosno da li će ona da raste ili opada. Takođe, upotrebljena je i relativna tačnost u kojoj se posmatra odnos stvarne cene i prediktovane. Najbolji rezultat dostigao je 57,3% tačnosti u predikciji kretanja cene, odnosno 99,39% relativne tačnosti.
Reference
[2] Blockchain. (2018, April 13). Retrieved April 13, 2018, from https://en.wikipedia.org/wiki/Blockchain
[3] Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014
[4] Augustyniak, L., Kajdanowicz, T., Szymanski, P., Tuliglowicz, W., Kazienko, P., Alhajj, R., & Szymanski, B.K. (2014). Simpler is better? Lexicon-based ensemble sentiment classification beats supervised methods. 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 924-929.
[5] S.-H. Na, Y. Lee, S.-H. Nam, and J.-H. Lee, “Improving opinion retrieval based on query-specific sentiment lexicon,” in Advances in Information Retrieval, ser. Lecture Notes in Computer Science, vol. 5478. Springer Berlin / Heidelberg, 2009, pp. 734–738.
[6] K., & Y. (2014, September 03). Convolutional Neural Networks for Sentence Classification. Retrieved April 13, 2018, from https://arxiv.org/abs/1408.5882
[7] Wikipedia contributors. (2018, March 26). Bag-of-words model. In Wikipedia, The Free Encyclopedia. Retrieved 20:07, April 13, 2018, from https://en.wikipedia.org/w/index.php?title=Bag-of-words_model&oldid=832576977
[8] Liu, Y., Qin, Z., Li, P., & Wan, T. (2017). Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis. Advances in Artificial Intelligence: From Theory to Practice Lecture Notes in Computer Science,192-201. Doi:10.1007/978-3-319-60042-0_22
[9] J. (2018, January 29). Jefferson-Henrique/GetOldTweets-python. Retrieved April 13, 2018, from https://github.com/Jefferson-Henrique/GetOldTweets-python
[10] Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford (2009)
[11] Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology 63(1), 163–173 (2012)
[12] SenticNet. (n.d.). Retrieved from https://sentic.net/
[13] A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, 2011, pp. 142–150.
