Semantic Analysis of Reviews About Organizations Using Machine Learning Methods

 
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Abstract

Semantic analysis of organizational reviews is a key tool for assessing customer satisfaction levels. Business entities should regularly conduct analysis and emotional sentiment investigation to delve deeper into the data and gain a more comprehensive understanding of their operations, including through the use of machine learning methods. Presently, deep learning-based methods are garnering increased attention due to their high efficiency. In this study, we will focus on sentiment analysis tasks. To perform sentiment analysis, we will employ machine learning methods, including various approaches to text vectorization, deep learning models, and natural language processing (NLP) algorithms.

General Information

Keywords: natural language processing, classification task, gradient boosting, recurrent neural networks, convolutional neural networks, BERT, GPT

Journal rubric: Data Analysis

OpenAlex citations: 3

OpenAlex trends: Sentiment Analysis and Opinion Mining, Advanced Text Analysis Techniques, Topic Modeling

Information about the work in OpenAlex

Number of citations: 3

Topics

Sentiment Analysis and Opinion Mining

This cluster of papers focuses on sentiment analysis and opinion mining, particularly in the context of social media and text mining. It covers various techniques such as lexicon-based methods, deep learning, aspect-based sentiment analysis, and machine learning for analyzing emotions and opinions in textual data from platforms like Twitter. The research also delves into emotion recognition and the impact of sentiment analysis on public perception.

Number of works: 67220  |  Total number of citations: 875220

Topic detailsв OpenAlex

Advanced Text Analysis Techniques

This cluster of papers focuses on the automatic extraction of keywords from textual data using various techniques such as graph-based methods, unsupervised approaches, and neural networks. The research explores the application of linguistic knowledge and statistical information to improve the accuracy of keyword extraction from documents.

Number of works: 44472  |  Total number of citations: 481484

Topic detailsв OpenAlex

Topic Modeling

This cluster of papers covers a wide range of advancements in natural language processing, including neural network architectures, word representation models, machine translation techniques, text classification algorithms, semantic similarity measures, named entity recognition methods, pretrained language models, sequence-to-sequence learning approaches, topic modeling strategies, and information retrieval systems.

Number of works: 170145  |  Total number of citations: 2443689

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2024140101

Received 26.02.2024

Accepted

Published

For citation: Platonov, E.N., Martynova, I.R. (2024). Semantic Analysis of Reviews About Organizations Using Machine Learning Methods. Modelling and Data Analysis, 14(1), 7–26. (In Russ.). https://doi.org/10.17759/mda.2024140101

© Platonov E.N., Martynova I.R., 2024

License: CC BY-NC 4.0

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Information About the Authors

Evgeniy N. Platonov, Candidate of Science (Physics and Matematics), Assistant Professor, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-8502-1350, e-mail: en.platonov@gmail.com

Irina R. Martynova, Student of the Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0009-0007-3140-2490, e-mail: irina.mart.r@gmail.com

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