Short Text Clustering Algorithms, Application and Challenges: A Survey

The number of online documents has rapidly grown, and with the expansion Web, document analysis, or text become an essential task for preparing, storing, visualizing mining documents. texts generated daily on social media platforms such as Twitter, Instagram Facebook are vast unstructured. Most these come in form short need special analysis because suffers from lack information sparsity. Thus, this topic attracted growing attention researchers data storing processing community knowledge discovery. Short clustering (STC) a critical automatically grouping various unlabelled into meaningful clusters. STC is necessary step many applications, including Twitter personalization, sentiment spam filtering, customer reviews other network-related applications. In last few years, natural-language-processing research concentrated attempted to overcome problems sparseness, dimensionality, information. We comprehensively review approaches proposed literature. Providing insights technological component should assist identifying possibilities challenges facing STC. To gain insights, we literature, journals, academic papers focusing techniques. contents study prepared by reviewing, analysing summarizing diverse types journals scholarly articles focus techniques five authoritative databases: IEEE Xplore, Web Science, Science Direct, Scopus Google Scholar. This focuses techniques: clustering, texts, pre-processing, representation, dimensionality reduction, similarity measurement evaluation.