SENTIMENT ANALYSIS TECHNOLOGY FOR USER FEEDBACK SUPPORT IN E-COMMERCE SYSTEMS BASED ON MACHINE LEARNING

Abstract

Context. The interaction between a company and its target audience has been studied for centuries. From the very beginning of commercial relations, the relationship between the service provider and the recipient has been valued almost above all else. Trade is built on trust and respect. The image of an entrepreneur is often more important than the product he sells. For hundreds of years, the relationship between the merchant and the buyer, the entrepreneur and the client has not lost its importance, and in the era of mass digitalization, the quality of the relationship between the company and the target audience of different sizes and professional feedback support with clients often start the success of e-business. To provide these additional tools and information technologies to help businessmen monitor e-business development opportunities in a specific location, as well as establish feedback with users through social networks and mass media. Obtaining such tools will significantly expand the vision of market opportunities for ebusiness, it will clarify which of them make sense to invest in, and which ones are not worth paying time for. Also see what idea has the future and what business model needs to be implemented/maintained/developed for the rapid development of territorial/interregional e-business. It will also help to understand which levers have the greatest effect for business changes: what not to touch, and what policies to change to ensure high speed in the implementation of the plan based on the analysis of relevant research results, for example, to receive: direct feedback from customers, the dynamics of changes in overall satisfaction or interest of the target audience and advantages/disadvantages from users using NLP analysis; support for the development of e-business in relation to the location of their enterprise and the best directions; – graphs of business development (improvement/deterioration) depending on the content of comments. Objective of the study is to develop information technology to support the development of e-business by analyzing business locations, processing feedback from users, analyzing and classifying customer feedback in real time from social networks: Twitter, Reddit, Facebook and others using deep learning and Natural methods. Language Processing of Ukrainian-speaking and Englishspeaking texts. Method. NLP-methods were used to analyze the opinions of users and customers. Among the methods of implementing the main functions of English-language news classification, the following machine learning methods are used: naive Bayesian classifier, logistic regression, and the method of support vectors. The Naive Bayes algorithm was used to classify Ukrainian-language user feedback, as it performs well on small amounts of data, is easy to train and operate, and works well with text data. Naive Bayes classifier is a very good option for our system and considering that the number of responses in the dataset is smaller compared to the averages. Results. A machine learning model was developed for the analysis and classification of Ukrainian- and English-language reviews from users of e-commerce systems. Conclusions. The created model shows excellent classification results on test data. The overall accuracy of the sentimental model for the analysis of Ukrainian-language content is quite satisfactory, 92.3%. The logistic regression method coped best with the task of analyzing the impact of English-language news on the financial market, which showed an accuracy of 75.67%. This is certainly not the desired result, but it is the largest indicator of all considered. The support vector method (SVM) coped somewhat worse with the task, which showed an accuracy of 72.78%, which is a slightly worse result than the one obtained thanks to the logistic regression method. And the naïve Bayesian classifier method did the worst with the task, which achieved an accuracy of 71.13%, which is less than the two previous methods.