Abstract
In this research, the focus is on recognizing 28 emotions in a text using the Roberta model, which is a state-of-the-art pre-trained language model that has achieved outstanding results in various natural language processing tasks. The study explores the effectiveness of the Roberta model for emotion recognition and compares it with other approaches, such as CNNs and RNNs. In addition, the research investigates the problem of toxicity detection, which involves identifying and flagging potentially harmful or offensive language in a given text. Various techniques for toxicity detection are considered, including supervised learning and deep learning methods. The study also explores the process of extracting key phrases and words from a text using machine learning algorithms. This involves applying NLP techniques such as part-of-speech tagging, named entity recognition, and text summarization. All of these methods are implemented and tested using a cloud service provided by Umai Cloud Services, a Kazakh startup company that offers machine learning and artificial intelligence solutions. The results of the study demonstrate the effectiveness of the Roberta model for emotion recognition and show promising results for toxicity detection and text summarization.