Development of a Web Application for Intelligent Analysis of Customer Reviews Using a Modified seq2seq Model with an Attention Mechanism

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Аннотация

Machine learning, and neural networks in particular, are having a huge impact on business and marketing by providing convenient tools for analytics and customer feedback. The article proposes an intelligent analysis of customer feedback based on the use of a modified seq2seq deep learning model. Since the basic seq2seq model has a significant disadvantage – the inability to concentrate on the main parts of the input sequence, the results of machine learning may give an inadequate assessment of customer feedback. This disadvantage is eliminated by means of a model proposed in the work called the “attention mechanism”. The model formed the basis for the development of a web application that solves the problem of flexible interaction with customers by parsing new reviews, analyzing them and generating a response to a review using a neural network.

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Evgeny Budaev

Financial University under the Government of the Russian Federation

Хат алмасуға жауапты Автор.
Email: esbudaev@fa.ru
ORCID iD: 0000-0002-3718-0282

Cand. Sci. (Eng.), Associate Professor, associate professor, Department of Data Analysis and Machine Learning, Faculty of Information Technology and Big Data Anlysis

Ресей, Moscow

Әдебиет тізімі

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2. Fig. 1. The basic architecture of the seq2seq model]

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3. Fig. 2. Classical architecture of GRU

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4. Fig. 3. Architecture of the seq2seq model with an attention mechanism

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5. Fig. 4. The organization’s goals tree

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6. Fig. 5. Diagram of use cases “Selling cosmetics on the marketplace”

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7. Fig. 6. Algorithm for the server part of the program

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8. Fig. 7. Part of the training sample]

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9. Fig. 8. The result of neural network training]

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10. Fig. 9. The algorithm of the server side of the application with the indication of frameworks and libraries

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11. Fig. 10. Part of the upload for testing the health of the server side of the application

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12. Fig. 11. The result of the program’s operation when testing the health of the server part of the application

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13. Fig. 12. Generated file

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