Development of automatic classification system for textual feedback in Russian language using machine learning
In today’s highly competitive environment enterprises could increase their flexibility and profitability by conducting analytical studies of textual customer feedback. One of the primary objectives of these studies is to determine the sentiment class of textual feedback in order to understand the consumer's overall evaluation of the product. This paper considers the task of classifying textual feedback into sentiment classes using machine learning techniques. Text data vectorization methods are studied and applied while solving the task. A comparative analysis of algorithms for classifying by sentiment classes is carried out: random forest algorithm, support vector machine, and naive Bayes classifier. The algorithm showing the best performance on quality evaluation metrics of the classification model is chosen. The subsystem of textual feedback classification that automates the process of text mining in the framework of enterprise product research is obtained.