Proceedings ArticleDOI
Detection of Fake and Provokative Comments in Social Network Using Machine Learning
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TLDR
Object of the project is creating model for identifying provocative and fake comments, and two artificial neural networks are created: definition of sarcasm and definition of sentiment analysis.Abstract:
Nowadays internet-trolls have big impact on other users, it interferes with comfortable use. Objective of the project is creating model for identifying provocative and fake comments. Science articles about detection of trolls by hand were searched for making the criteria of relevant comments selection. To achieve the goal there were created two artificial neural networks: definition of sarcasm and definition of sentiment analysis. The program result is datasets of troll comments and fake comments, statistic and diagrams of definition.read more
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Book ChapterDOI
Comment Filtering Based Explainable Fake News Detection
TL;DR: In this article, the authors proposed a semi-supervised approach to classify the comments in junky or utility comments classes using the basic definition of spam filtering, which can be used for different uses using different criteria.
Proceedings ArticleDOI
Sentiment Analysis of Social Media Comments based on Random Forest and Support Vector Machine
TL;DR: A model based on algorithm of Random Forest and SVM is developed to classify the sentiment in social media comments, which would facilitate the decision-making process and various measures can be taken to enhance the management of government.
Journal ArticleDOI
Identification of cyberbullying by neural network methods
TL;DR: In this article, in order to identify trolls, users are grouped together, this association is carried out by identifying a similar way of communication and a special type of neural networks, namely self-organizing Kohonen maps.
Journal ArticleDOI
Application of Thematic Modeling Methods in Text Topic Recognition Tasks to Detect Telephone Fraud
E. S. Pleshakova,Sergei T. Gataullin,A. V. Osipov,Ekaterina V. Romanova,Anna Sergeevna Marun'ko +4 more
TL;DR: The application of several thematic modeling models, in particular LDA, LSI and NMF, for text topic recognition are presented, and directions for improving text processing algorithms are proposed.
References
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Journal ArticleDOI
Social Media Sentiment Analysis using Machine Learning and Optimization Techniques
TL;DR: The research uses a hybrid method of using Swarm Intelligence optimization algorithms with classifiers to classify a speaker's or a writer’s attitude towards various events or topics and arranging data into positive, negative or neutral categories.