Text sentiment analysis in banking

The paper presents the author's approach to solving the problem of sentiment analysis of online Russian-language messages about the activities of banks. The study data are customer reviews about banks in general and their products, services and quality of service posted on the Banki.ru portal. In this paper, the problem of text sentiment analysis is considered as a binary classification task based on a set of positive and negative reviews. A vector model with a tf-idf weighting scheme was used to represent the collected and preprocessed texts. The following algorithms with the selection of optimal parameters on the grid were used for binary classification task: naive Bayesian classifier, support vector machine, logistic regression, random forest and gradient boosting. Standard statistical metrics, such as accuracy, completeness, and F-measure, were used to evaluate the quality of solving the classification problem. For the indicated metrics, the best results were obtained on the classification model developed with the use of Support Vector Machine. Thematic text modeling was also carried out using the Dirichlet latent placement method to define the most typical topics of customer messages. As a result, it was concluded that the most popular message topics are "cards" and "quality of service". The obtained results can be used in the activities of banks to automate its reputation monitoring in the media and when routing client requests to solve various problems. When solving problems, the features of the Python programming language were actively used, namely, libraries for web scraping, machine learning, and natural language processing.