# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering  
Clustering is a type of data analysis that has been widely studied; it aims to group a collection of data objects or items into subsets or clusters [37]. Specifically, the main goal of clustering is to generate cohesive and identical groups of similar data elements by grouping related data points into unified clusters. All the documents or objects in the same cluster must be as similar as possible [38]. In other words, similar documents in a cluster have similar topics so that the cluster is coherent internally. Distinctions between each cluster are notable. Documents or objects in the same cluster must be as different from those in the other clusters as possible. Text clustering is essential to many real-world applications, such as text mining, online text organisation and automatic information retrieval systems. Fast and high-quality document clustering greatly aids users in successfully navigating, summarizing and organizing large amounts of disorganized data. Furthermore, it may determine the structure and content of previously unknown text collections. Clustering attempts to automatically group documents or objects with similar clusters by using various similarity/distance measures [39]. Differentiating between clustering and classification of documents is crucial [40]. Still, the difference between the two may be unclear because a set of documents must be split into groups in both cases. In general, labelled training data are supplied during classification; however, the challenge arises when attempting to categorize test sets, which consist of unlabelled data, into a predetermined set of classes. In most cases, the classification problem may be handled using a supervised learning method [41; 42]. As mentioned above, one of the significant challenges in clustering is grouping a set of unlabelled and non-predefined data into similar groups. Unsupervised learning methods are commonly used to solve the clustering problem. Furthermore, clustering is used in many data fields that do not rely on predefined knowledge of the data, unlike classification, which requires prior knowledge of the data [43]. Short texts are becoming increasingly common as online social media platforms such as Instagram, Twitter and Facebook increase in size. They have very minimal vocabulary; many words even appear only once. Therefore, STC significantly affects semantic analysis, demonstrating its importance in various applications, such as IR and summarisation [2]. However, the sparsity of short text representation makes the traditional clustering methods unsatisfying. This is due to the sparsity problems caused by each short text document only containing a few words. Short text data contain unstructured sentences that lead to massive variance from regular texts' vocabulary when using clustering techniques. Therefore, self-corpus-based expansion is presented as a semantically aligned substitutional approach by defining and augmenting concepts in the corpus using clustering techniques [44] or topics based on the probability of frequency of the term [45]. However, dealing with the microblogging data is challenging for any of these methods because of their lack of structure and the small number of co-occurrences among words [46]. Several strategies have been proposed to alleviate the sparsity difficulties caused by lack of context, such as corpus-based metrics [47] and knowledge-based metrics [25; 48]. One of these simple strategies concentrates on data-level enhancements. The main idea is to combine short text documents to create longer ones [49]. For aggregation, related models utilize metadata or external data [48]. Although these models can alleviate some of the sparsity issues, a drawback remains. That is, these models rely on external data to a large extent. STC is more challenging than traditional text clustering. Representations of the text in the original lexical space are typically sparse, and this problem is exacerbated for short texts [50].