Sentiment Analysis of User Texts with Machine Learning Methods

This paper explores the application of machine learning methods for sentiment analysis of user-generated texts in the Russian social network VKontakte. The sentiments of millions of users could be monitored and analyzed in real time, that facilitates prompt decision making and forecasting of social processes. Textual data, including posts and comments, were collected via the VK API. The preprocessing pipeline involved text cleaning, lemmatization, stop-word removal, and TFIDF vectorization. Several classification models were tested, including logistic regression, random forest, and naïve Bayes, as well as deep learning models such as LSTM and Transformers (RuBERT). The naïve Bayes classifier demonstrated the best performance in terms of recall and overall metric balance. Sentiment analysis results revealed that the majority of user texts were neutral or positive, with only a small portion being negative. The paper includes visualizations and statistical summaries of sentiment distribution. The study confirms the effectiveness of classical machine learning methods for processing and analyzing textual data in Russian social networks.