# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 1 Introduction  
Recently, the number of text documents on the Internet has increased significantly and rapidly. The rapid development of mobile devices and Internet technologies has encouraged users to search for information, communicate with friends and share their opinions and ideas on social media such as Twitter, Instagram, and Facebook and search engines such as Google. The texts generated every day in social media are vast and unstructured data [1]. Most of these generated texts come in the form of short texts and need special analysis compared with formally written ones [2; 3]. Short texts can be found on the Internet, including on social media, in product descriptions, in advertisement text, on questions and answers (Q&A) websites [4] and in many other applications. Short texts are distinguished by a lack of context, so finding knowledge in them is difficult. This issue motivates researchers to develop novel, effective methods. Examples of short texts can be found in various contexts, like tweets, search inquiries, chat messages, online reviews and product descriptions. Short text also presents a challenge in clustering owing to its chaotic nature,which typically contains noise, slang, emojis, misspellings, abbreviations and grammatical errors. Tweets are a good example of these challenges. In addition, short text represents various facets of people's daily lives. As an illustration, Twitter generates 500 million tweets per day. These short texts can be used in several applications, such as trend detection [5], user profiling [6], event exploration [7], system recommendation [8], online user clustering [9] and cluster-based retrieval [2; 10]. With the vast amount of short texts being added to the web every day, extracting valuable information from short text corpora by using data-mining techniques is essential [11; 12]. Among the many different data-mining techniques, clustering stands out as a unique technique for short text that provides the exciting potential to automatically recognize valuable patterns from a massive, messy collection of short texts [13]. Clustering techniques focus on detecting similarity patterns in corpus data, automatically detecting groups of similar short texts, and organising documents into semantic and logical structures. Clustering techniques help governments, organisations and companies monitor social events, interests and trends by identifying various subjects from user-generated content [14; 15]. Many users can post short text messages, image captions, search queries and product reviews on social media platforms. Twitter sets a restriction of 280 characters on the length of each tweet [16], and Instagram sets a limit of 2200 characters for each post [17]. Clustering short texts (forum titles, result snippets, frequently asked questions, tweets, microblogs, image or video titles and tags) within groups assigned to topics is an important research subject. Short text clustering (STC) has undergone extensive research in recent years to solve the most critical challenges to the current clustering techniques for short text, which are data sparsity, limited length and high-dimensional representation [18; 19; 20; 21; 22; 23]. Applying standard clustering techniques directly to a short text corpus creates issues. The accuracy is worse when using traditional clustering techniques such as the K-means [24] technique to group short text data than when using the same method to group regular-length documents [25]. One of the reasons is that standard clustering techniques such as K-means [24] and DBSCAN [26] depend on methods that measure the similarity/distance between data objects and accurate text representations [25]. However, the use of standard text representation methods for STC, such as a term frequency inverse-document-frequency (TF-IDF) vectors or bag of words (BOW) [27], leads to sparse and high-dimensional feature vectors that are less distinctive for measuring distance [18; 28; 29]. Therefore, using dimensionality reduction as an optional step of the STC system is essential.