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Comparison of Approaches to Building Ensemble Models in the Problem of Analysis of User Reviews

Student: Grigoriy Khomyuk

Supervisor: Liudmila Zhukova

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2021

Analyzing generally accessible information provided by users in social media might improve customer service in various manufacturing companies and companies that provide services to individuals. Such information might be presented by customers’ reviews about the product or service on different websites. Basic machine learning algorithms often assist heavily in solving the problem of identifying the negativity of the feedback. The purpose of the work is to compare the approaches to constructing ensemble models and to study the effectiveness of such models in the problems of classifying customer reviews. The object of this research is ensemble methods for constructing models that can outperform single basic algorithms in forecasting quality. During the research, methods of preprocessing text data, methods of ensemble classifiers were studied and described, and a comparative analysis of the models was carried out according to various metrics of forecasting quality. The result of this study is the classification model with high predictive power, the approach to the construction of which can be used in various tasks of unstructured data classification.

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