cosine similarity

Intellectual Analysis of Textual Data in Social Networks Using BERT and XGBoost

This article presents a comprehensive approach to sentiment analysis in social networks by leveraging modern text processing methods and machine learning algorithms. The primary focus is the integration of the Sentence-BERT model for text vectorization and XGBoost for sentiment classification. Using the Sentiment140 dataset, an extensive study of text messages labeled with sentiment annotations was conducted. The Sentence-BERT model enables the generation of high-quality vector representations of textual data, preserving both lexical and contextual relationships between words.

Comparison and Clustering of Textual Information Sources Based on the Cosine Similarity Algorithm

This article presents a study aimed at developing an optimal concept for analyzing and comparing information sources based on large amounts of text information using natural language processing (NLP) methods. The object of the study was Telegram news channels, which are used as sources of text data. Pre-processing of texts was carried out, including cleaning, tokenization and lemmatization, to form a global dictionary consisting of unique words from all information sources.