EXPERIMENTAL COMPARISON OF TEXT VECTORIZATION METHODS FOR SENTIMENT ANALYSIS TASK
TL;DR
Recommendations are given for choosing a vectorization method depending on the data volume and system performance requirements in real time, and classical methods with linear models demonstrate competitive quality with significantly lower computational costs on small datasets.
Abstract
The paper presents a comparative analysis of the effectiveness of various text vectorization methods for the task of Sentiment Analysis of Russian-language reviews. The study covers classical frequency-based approaches (TF IDF, n-grams), statistical models (Word2Vec, FastText), and a contextual method based on the pre-trained BERT language model. The practical part of the research includes the implementation of text processing and classification pipelines using logistic regression and Naive Bayes classifiers. Experiments are conducted on a dataset of Russian-language reviews from a marketplace. Key comparison metrics are classification accuracy (accuracy, F1-score) and model training/inference time. The results show that on small datasets, classical methods with linear models demonstrate competitive quality with significantly lower computational costs. Contextual BERT embeddings show the best quality on the test set; however, their use is justified only with sufficient data volumes and the absence of strict real-time inference constraints. Based on the analysis, recommendations are given for choosing a vectorization method depending on the data volume and system performance requirements in real time.