# Analysis of Short Texts Using Intelligent Clustering Methods
## 1 Introduction  
Practical text data classification and comprehension [1, 2, 3] are crucial to a range of applications, such as social media analysis [4, 5, 6, 7, 8], recommendation systems, and automated content moderation [9, 10, 11]. During the last decade, extensive efforts have been made towards developing methods to process and extract data from short texts with restricted and condensed information. Among the most potent approaches in this area are BERT, TF + IDF, and the integrated LDA + BERT + AE method. BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking method in deep learning that allows models to learn contextual relations within the text more efficiently. TF + IDF, an ancient statistical technique, is used to quantify the importance of words in a document and is thus avaluable asset for information filtering and identification of essential topics. The combined LDA + BERT + AE method promises to combine the depth and width of text understanding using LDA (Latent Dirichlet Allocation) topic modeling, BERT contextual understanding, and AE (Autoencoder) data dimensionality reduction and optimization. The importance of the analysis of short texts with the help of intelligent clustering methods [12] stems from the growing need for the rapid processing and understanding of vast amounts of compressed text data, characteristic of modern digital communication. With the growth of social networks, websites, and mobile applications, the number of short messages such as tweets, statuses, and comments is increasing, for which natural language processing systems must improve the speed and accuracy of analysis. These messages contain valuable information about public opinion, user preference, and current trends, and therefore, their analysis is crucial in marketing, brand management, political analysis, and many other fields. Hence, developing and optimizing models such as BERT, TF + IDF, and LDA + BERT + AE to deal with such texts efficiently and accurately is of the topmost priority in artificial intelligence and NLP [13; 14; 15]. This study aims to improve the quality and hasten the processing of text clustering data so that intelligent clustering methods can be employed for more precise and efficient retrieval of helpful information. Particular emphasis is placed on their flexibility and utility across applications where traditional approaches would face difficulties because of the paucity of data quantity or heterogeneity. The results of this work can offer significant improvements in the field of short text analysis, providing a deeper and more accurate understanding of the content and helping to implement more effective NLP systems. The importance of this topic is increased in the digital era, with data volumes growing exponentially. Short texts such as tweets, user reviews, and social media comments represent a significant portion of this content. Traditional analysis methods [16; 17; 18] are often ineffective for working with such formats due to their brevity and high concentration of meaning in a limited number of words. This challenges researchers to select suitable data processing tools and adapt them to the specific requirements of short text formats. The impact of contextual analysis on the quality and accuracy of clustering is also considered. Modern technologies such as BERT [19; 20] offer a revolutionary approach to understanding language, allowing systems to better deal with the ambiguities and complexities of natural language. Integrating BERT with LDA [21; 22] and AE [23; 24; 25] within a single solution opens up new opportunities to improve analysis accuracy through a deeper understanding of text structures and semantics. This work aims to demonstrate how such combined approaches can enhance short texts' clustering and classification processes, considering their unique characteristics and needs. Thus, the study offers a comprehensive view of the problem of analyzing short texts and examines promising directions for developing clustering technologies based on data mining.