# Analysis of Short Texts Using Intelligent Clustering Methods
## 1 Introduction
The results obtained in this work suggest a significant contribution to natural language processing. They can be used to create more efficient and adaptive NLP systems, capable of coping with a wide range of tasks in the modern information world. In [26], the authors discuss the importance of analyzing short texts such as social media posts for clustering and knowledge extraction. They review different approaches to short text clustering (STC) to overcome the problems of sparsity, high dimensionality, and lack of information and analyze and summarize research results from five authoritative databases. In [27], the authors of the Chinese Co-Learning Clustering (COTC) method use the advantages of BERT and TF-IDF to cluster working texts. This approach uses the mutual training of two models, which can improve the clustering quality to combine semantic and keyword information. In [28], the researchers propose the GloCOM model, which uses global context clustering to improve topic study in text retrieval. This approach can effectively solve the problem of data sparsity inherent in certain texts. In [29], the POTA method is developed, which uses the attention and traffic mechanism to generate reliable pseudo-labels. This can improve the learning of the presented materials and increase the accuracy of clustering the necessary texts. The authors of [30] investigated the possibility of using large language models (LLMs) for clustering sufficient texts. They applied generative models to create interpretable clusters, which can achieve greater consistency between the clustering effects and human resources. In [31], the author proposed the AECL method, which uses the attention mechanism and contrast learning to create more discriminative representations. This makes it possible to effectively solve the problem of false negative examples and improve the quality of text clustering. The main contributions of this study include the development of a hybrid architecture (LDA + BERT + AE) that integrates topic modeling, contextual embedding, and dimensionality reduction into a unified framework specifically designed for short text clustering. A weighted concatenation mechanism is introduced, with a tunable parameter \(\gamma\), which balances the contribution of topic-based and semantic features. The proposed method is extensively compared with traditional models such as TF-IDF and standalone BERT using multiple clustering quality metrics (Silhouette, Adjusted Rand Index, V-Measure) and demonstrates significantly improved performance. The approach is validated on a real-world dataset of categorized news articles, achieving a high accuracy (98%) and F1-score (0.9), confirming its practical effectiveness. Furthermore, the implementation is made publicly available to support reproducibility and further research in the field of hybrid embeddings for natural language processing. Despite the high popularity of individual methods such as BERT, TF-IDF, and LDA, each has certain limitations in the task of short text clustering. In particular, TF-IDF does not take into account the context, LDA does not reflect the semantic relationships between words, and BERT weakly separates topics. In this paper, we propose a hybrid method, LDA + BERT + AE, which can overcome these limitations by combining the methods and utilizing a learnable feature combination.