Development of and research on machine learning algorithms for solving the classification problem in Twitter publications

Makarov I.S., Bagantsova E.R., Iashin P.A., Kovaleva M.D., Gorbachev R.A.
 pdf (457K)

Posts on social networks can both predict the movement of the financial market, and in some cases even determine its direction. The analysis of posts on Twitter contributes to the prediction of cryptocurrency prices. The specificity of the community is represented in a special vocabulary. Thus, slang expressions and abbreviations are used in posts, the presence of which makes it difficult to vectorize text data, as a result of which preprocessing methods such as Stanza lemmatization and the use of regular expressions are considered. This paper describes created simplest machine learning models, which may work despite such problems as lack of data and short prediction timeframe. A word is considered as an element of a binary vector of a data unit in the course of the problem of binary classification solving. Basic words are determined according to the frequency analysis of mentions of a word. The markup is based on Binance candlesticks with variable parameters for a more accurate description of the trend of price changes. The paper introduces metrics that reflect the distribution of words depending on their belonging to a positive or negative classes. To solve the classification problem, we used a dense model with parameters selected by Keras Tuner, logistic regression, a random forest classifier, a naive Bayesian classifier capable of working with a small sample, which is very important for our task, and the k-nearest neighbors method. The constructed models were compared based on the accuracy metric of the predicted labels. During the investigation we recognized that the best approach is to use models which predict price movements of a single coin. Our model deals with posts that mention LUNA project, which no longer exist. This approach to solving binary classification of text data is widely used to predict the price of an asset, the trend of its movement, which is often used in automated trading.

Keywords: сryptocurrency, Twitter, machine learning, natural language processing, vectorization, dense model, logistic regression, random fores classifier, KNN, naive Bayes classifier
Citation in English: Makarov I.S., Bagantsova E.R., Iashin P.A., Kovaleva M.D., Gorbachev R.A. Development of and research on machine learning algorithms for solving the classification problem in Twitter publications // Computer Research and Modeling, 2023, vol. 15, no. 1, pp. 185-195
Citation in English: Makarov I.S., Bagantsova E.R., Iashin P.A., Kovaleva M.D., Gorbachev R.A. Development of and research on machine learning algorithms for solving the classification problem in Twitter publications // Computer Research and Modeling, 2023, vol. 15, no. 1, pp. 185-195
DOI: 10.20537/2076-7633-2023-15-1-185-195
URL: http://crm-en.ics.org.ru/journal/article/3297/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"

Copyright © 2009–2024 Institute of Computer Science