Since roughly 80% of data in the world resides in an unstructured format (link resides outside ibm.com), text mining is an extremely valuable practice within organizations. Examples of semi-structured data include XML, JSON and HTML files. While it has some organization, it doesn’t have enough structure to meet the requirements of a relational database.
Semi-structured data: As the name suggests, this data is a blend between structured and unstructured data formats. It can include text from sources, like social media or product reviews, or rich media formats like, video and audio files. Unstructured data: This data does not have a predefined data format. Structured data can include inputs such as names, addresses, and phone numbers. Structured data: This data is standardized into a tabular format with numerous rows and columns, making it easier to store and process for analysis and machine learning algorithms. Depending on the database, this data can be organized as: Text is a one of the most common data types within databases. By applying advanced analytical techniques, such as Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, companies are able to explore and discover hidden relationships within their unstructured data. Communications of the ACM, 27(11), 1134–1142.Text mining, also known as text data mining, is the process of transforming unstructured text into a structured format to identify meaningful patterns and new insights. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (pp. CRYSTAL: Inducing a conceptual dictionary. Soderland, S., Fisher, D., Aseltine, J., & Lehnert, W. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining. Learning to extract text-based information from the World Wide Web. thesis (Technical Report UM-CS-1996-087). Learning text analysis rules for domain-specific natural language processing. Proceedings of the Eleventh National Conference on Artificial Intelligence (pp. Automatically constructing a dictionary for information extraction tasks. Learning logical definitions from relations. Proceedings of the Sixth Message Understanding Conference. A theory and methodology of inductive learning, In Michalski, Carbonell, & Mitchell (Eds.), Machine learning: An artificial intelligence approach. Proceedings of ACM-SIGIR Conference on Information Retrieval (pp. 
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