ANALYSIS AND COMPARISON OF RESULTS IN THE EVALUATION OF SOCIAL NETWORK MESSAGES
Received: 2026-06-21 11:49:45
Published: 2025-12-21
Abstract
This paper analyzes recent developments in the automatic evaluation of textual and multimodal content (posts, comments, memes, etc.) on social networks. It reviews models used in hate speech and target group detection, sentiment and aspect-based sentiment analysis, and general classification tasks (classical ML models, deep learning, transformer-based and multimodal architectures) and compares their performance. The results show that transformer and multimodal approaches outperform others, while the main limitation remains the lack of Uzbek-language resources (corpora, models, embeddings).
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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