Geospatial information is hidden in most unstructured data. In the research community, there is considerable interest in techniques developed to analyze a variety of texts in spatial and temporal contexts. Natural language processing (NLP) methods have significantly advanced due to advances in artificial intelligence (AI), digital text data availability, and increased computational power. In today’s geospatial world, there are a number of applications that have been developed in the past by the combination of natural language processing techniques with geospatial methods. NLP has been extensively used to identify events, places, entities, and any spatiotemporal pattern in any geographic phenomena. The involvement of NLP techniques may range from basic pre-processing methods to any machine learning algorithms or very advanced techniques that can be utilized to infer and interpret semantic information from a large volume of text. The purpose of this book chapter is to highlight the significance of NLP techniques in the domain of Geospatial Analytics. Geotext is discussed in this work, along with how it is processed using geospatial analytics. In context of geospatial analytics with natural language processing, several tasks were described, including geoparsing, which extracts place names from text; geospatial information extraction, which extracts geographical entities from texts; and geographic question answer, which answers questions related to locations. The use of natural language processing to develop comprehensive analytics for a wide range of topics in the future is demonstrated in this paper.