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    Materials from DonNTU Masters

    1. Analysis of Text Sentiment Using Machine Learning Methods

      Authors: Romanov A. S., Vasilieva M. I., Kurtukova A. V., Meshcheryakov R. V.

      Description: This article presents the results of research on the methodology of text sentiment analysis using machine learning methods, such as support vector machines, naive Bayes classifier, and random forest methods. It provides a review of research, methods, and software products in the field of text sentiment analysis. It describes the stages of modeling the process of conducting experiments and determining text sentiment, provides descriptions of created text corpora and dictionaries, and presents the obtained research results.

      Source: A. S. Romanov, M. I. Vasilieva, A. V. Kurtukova, R. V. Meshcheryakov. Analysis of Text Sentiment Using Machine Learning Methods

    2. Determining Text Sentiment Based on the "Bag-of-Words" Model

      Authors: Pilipenko A. S., Kolomoytseva I. A.

      Description: Determining text sentiment based on the Bag-of-Words model. The problem of determining text sentiment using the Bag-of-Words model is discussed, and the advantages and disadvantages of using this model for solving the task are indicated.

      Source: A. S. Pilipenko, I. A. Kolomoytseva. Determining Text Sentiment Based on the Bag-Of-Words Model // Informatics, Control Systems, Mathematical and Computer Modeling (IUSMKM – 2020) / Proceedings of the XI International Scientific and Technical Conference. – Donetsk, DonNTU – 2020, pp. 77 – 81.

    Scientific Papers and Articles

  1. TEXT SENTIMENT ANALYSIS: MODERN APPROACHES AND EXISTING PROBLEMS

    Authors: Semina T. A.

    Description: This article is dedicated to the review of works on sentiment analysis, one of the current directions in natural language processing.

    Source: Journal of Social Sciences and Humanities. Domestic and Foreign Literature. Series 6, Linguistics: Abstract Journal

  2. TEXT SENTIMENT ANALYSIS FROM SOCIAL NETWORKS BASED ON MACHINE LEARNING METHODS FOR MONITORING PUBLIC SENTIMENTS

    Authors: Smetanin Sergey Igorevich

    Description: This work is dedicated to the development of models, methods, and software complexes designed for monitoring public sentiments by analyzing the sentiment of textual posts from social networks written in Russian.

    Source: Dissertation for the degree of Candidate of Computer Science by Sergey Igorevich Smetanin

  3. REVIEW OF TEXT SENTIMENT ANALYSIS SYSTEMS IN RUSSIAN

    Authors: Menshikov Ilya Leonidovich, Kudryavtsev Alexander Genrikhovich

    Description: This article reviews and analyzes the most popular sentiment analysis systems for the Russian language.

    Source: Menshikov, I. L. Review of Text Sentiment Analysis Systems in Russian / I. L. Menshikov, A. G. Kudryavtsev. — Text: direct // Young Scientist. — 2012. — № 12 (47). — P. 140-143. — URL: https://moluch.ru/archive/47/5951/ (accessed: 09/18/2023).

Technical and Reference Literature

  1. BUILDING A NEURAL NETWORK MODEL FOR TEXT DATA ANALYSIS

    Authors: Strelets Andrei Ivanovich, Chernikova Elena Andreevna, Doronichev Nikita Andreevich, Sychev Maxim Igorevich

    Description: This article describes key aspects of building a neural network model for text data analysis. An evaluation of the constructed model is provided.

    Source: E-Scio Journal

  2. METHODS OF TEXT DATA CLASSIFICATION BY TOPICS

    Authors: Strelets A. I., Ivannikov V. S., Orlov A. A., Atavina A. V.

    Description: This article describes methods of text data classification by topics. Text classification is an important and relevant area in information processing and machine learning. The article analyzes and studies existing solutions in this field, examines key aspects of various methods, and provides a comparison. Based on the conclusions drawn, specificities of applying these algorithms are outlined.

    Source: International Journal of Humanities and Natural Sciences

  3. Sentiment analysis: extracting opinions, feelings and emotions

    Authors:Bing Liu

    Description: Review of methods for analyzing the tonality of a text with a focus on extracting opinions and emotions

    Source:E-book.

  4. Opinion mining and sentiment analysis

    Authors:Bo Pang, Lillian Lee

    Description: Consideration of various methods of opinion mining and tonality analysis, including approaches based on machine learning.

    Source: E-book.

  5. Applied Natural Language processing using Python

    Authors:Lane, Howard, Hapke

    Description: A practical look at natural language processing using Python, including tools and techniques for working with text data.

    Source: E-book.

  6. Instagram Facebook, Twitter, LinkedIn, Instagram, GitHub and other data mining:

    Authors:Matthew A. Russell

    Description: Aspects of data mining in social networks with a bias in the analysis of tonality in social media.

    Source: E-book.

  7. Natural language processing in practice

    Authors:Lane, Howard, Hapke

    Description: A practical approach to solving natural language processing problems using real examples and tasks.

    Source: E-book.

  8. Python Machine Learning

    Authors:Sebastian Raschka, Vahid Mirjalili

    Description: An extensive guide to machine learning in Python covering aspects of text analysis.

    Source: E-book.

  9. Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data

    Authors:Dipanjan Sarkar

    Description: Focus on using Python to analyze text data and extract valuable insights.

    Source: E-book.

  10. Practical Machine Learning for Computer Vision

    Authors:Martin Gorner, Ryan Gillard, Valliappa Lakshmanan

    Description: It is focused on the application of machine learning in the field of computer vision, but may contain useful methods for analyzing text and tonality.

    Source: E-book.

  11. Introduction to Machine Learning with Python: A Guide for Data Scientists

    Authors:Andreas C. Muller, Sarah Guido

    Description: It is focused on the application of machine learning in the field of computer vision, but may contain useful methods for analyzing text and tonality.

    Source: E-book.

  12. Text Mining in Practice with R

    Authors:Ted Kwartler

    Description: Practical aspects of text mining using R.

    Source: E-book.

  13. Emotion and Sentiment Analysis: Exploring the Latent Semantic Structure of Affect

    Authors:Khurshid Ahmad

    Description: Focus on the analysis of emotions and moods in the text through a latent semantic structure.

    Source: E-book.

  14. Text Mining and Visualization: Case Studies Using Open-Source Tools

    Authors:Taylor Arnold, Lauren Tilton

    Description: Examples of using open tools for text mining and data visualization.

    Source: E-book.

  15. Sentiment Analysis in Social Networks

    Authors:Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu

    Description: Tonality analysis in social networks, including methods and applications.

    Source: E-book.

  16. Text Mining: Applications and Theory

    Authors:Michael W. Berry, Jacob Kogan

    Description: Theoretical aspects and applications of text mining.

    Source: E-book.

  17. Python Text Processing with NLTK 2.0 Cookbook

    Authors:Jacob Perkins

    Description: Practical recipes for text processing using the NLTK library in Python.

    Source: E-book.

  18. Introduction to Information Retrieval

    Authors:Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze

    Description: Introduction to information retrieval and information retrieval.

    Source: E-book.

  19. Mastering Natural Language Processing with Python

    Authors:Deepti Chopra, Nisheeth Joshi, Iti Mathur

    Description: An in-depth look at natural language processing using Python.

    Source: E-book.

  20. Sentiment Analysis: A Definitive Guide

    Authors:Kaggle Inc

    Description: A comprehensive guide to tonality analysis.

    Source: E-book.

  21. Sentiment Analysis: A Definitive Guide

    Authors:Jalaj Thanaki

    Description: Natural language processing using Python.

    Source: E-book.

  22. Mining Text Data

    Authors:Charu C. Aggarwal

    Description: Methods of mining text data.

    Source: E-book.

  23. Text Analytics with R: A Practical Guide to Extracting Insights from Text Data

    Authors:David Robinson

    Description: A guide to using R for text analytics and extracting information from text data.

    Source: E-book.

  24. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning

    Authors:Benjamin Bengfort, Rebecca Bilbro

    Description: Application of text analysis using Python to create language-based products using machine learning.

    Source: E-book.

  25. Text Analysis with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data

    Authors:Dipanjan Sarkar

    Description: A practical approach to analyzing text data using Python.

    Source: E-book.

  26. Handbook of Natural Language Processing

    Authors:Nitin Indurkhya, Fred J. Damerau

    Description: Handbook of Natural Language Processing.

    Source: E-book.

  27. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

    Authors:Dan Jurafsky, James H. Martin

    Description: Introduction to Natural Language processing, computational linguistics and speech recognition.

    Source: E-book.

  28. Machine Learning for Dummies

    Authors:John Paul Mueller, Luca Massaron

    Description: Introduction to Machine learning for beginners.

    Source: E-book.

  29. Python Deep Learning

    Authors:Ivan Vasilev, Daniel Slater, Gianmario Spacagna

    Description: Deep learning in Python.

    Source: E-book.

  30. Natural Language Processing: Python and NLTK

    Authors:Nitin Hardeniya

    Description: Natural language processing using Python and NLTK library.

    Source: E-book.

  31. Foundations of Machine Learning

    Authors:Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

    Description: The book offers fundamental concepts and methods of machine learning, without focusing on a specific programming language.

    Source: E-book.

  32. Natural Language Processing: A Survey

    Authors:Hamid R. Arabnia, Kevin Daimi

    Description: This review provides a broad look at natural language processing and may contain references to various programming languages.

    Source: E-book.

  33. Machine Learning for Text

    Authors:Charu C. Aggarwal

    Description: The book covers machine learning methods applied to text analysis, with an emphasis on general principles.

    Source: E-book.

  34. Introduction to Machine Learning

    Authors:Ethem Alpaydin

    Description: This book provides an introduction to machine learning and can be useful for understanding the basic principles regardless of the programming language used.

    Source: E-book.

  35. Sentiment Analysis: A Multifaceted Approach

    Authors:Sabine Graf, Thanh Tho Quan, Ralf Krestel

    Description: This book addresses various aspects of tonality analysis, including information extraction, classification, and cross-domain analysis.

    Source: E-book.

  36. Sentiment Analysis: A Multifaceted Approach

    Authors:Sabine Graf, Thanh Tho Quan, Ralf Krestel

    Description: This book addresses various aspects of tonality analysis, including information extraction, classification, and cross-domain analysis.

    Source: E-book.

  37. Sentiment Analysis and Opinion Mining

    Authors:Vijayshri Nagaraj, Rani S.

    Description: This book introduces the basic concepts of tonality analysis focused on mining reviews and opinions.

    Source: E-book.

  38. Sentiment Analysis: Methods and Applications

    Authors:Ahmed Abbasi, Hsinchun Chen

    Description: The book provides an overview of the methods and applications of tonality analysis, as well as examines the challenges and prospects in this area.

    Source: E-book.

  39. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

    Authors:Vasileios Hatzivassiloglou, Kathleen R. McKeown

    Description: This work focuses on the use of subjectivity summation methods for analyzing the tonality of texts.

    Source: E-book.

  40. Sentiment Analysis: Capturing Favorability Using Natural Language Processing

    Authors:Sharmila D. Deshpande

    Description: The author explores the methods of tonality analysis using natural language, highlighting aspects of text processing and understanding.

    Source: E-book.

  41. Fine-grained Sentiment Analysis: Uncovering the Fine-grained Sentiment in Texts

    Authors:Bing Liu, Lei Zhang

    Description: This book focuses on a detailed analysis of tonality, including approaches to identifying subtle differences of opinion.

    Source: E-book.

  42. Subjectivity and Sentiment Analysis: From Words to Discourse

    Authors:Janyce Wiebe, Theresa Wilson

    Description: The authors provide an overview of the topic of subjectivity and tonality analysis, taking into account both individual words and discourse as a whole.

    Source: E-book.

  43. The Oxford Handbook of Computational Linguistics

    Authors:Ruslan Mitkov

    Description: This handbook presents key aspects of computational linguistics, including methods for analyzing tonality in a text.

    Source: E-book.

Specialized websites and portals

  1. CIT-FORUM

    Description: The largest archive of scientific and practical information in all areas of computer science

  2. CYBERFORUM

    Description: Forum of programmers and system administrators. Free help in solving problems in programming, mathematics, physics and other sciences, solving problems with computers, operating systems.

Specialized Websites and Portals

  1. CIT-FORUM

    Description: The largest archive of scientific and practical information in all areas of computer science.

  2. CYBERFORUM

    Description: A forum for programmers and system administrators. Free assistance in solving problems related to programming, mathematics, physics, and other sciences, as well as troubleshooting computer and operating system issues.