Text Mining and Analytics

About this course: This course will explain the main techniques for mining and analyzing text data, discovering interesting patterns, extracting useful knowledge, and supporting decision-making. In order to analyze text data in detail, it is necessary to understand the text of natural language. This is a hard work for computers. However, it has been proved that many statistical methods are effective for robust analysis of "shallow" text data for pattern discovery and knowledge discovery. Learn the basic concepts, principles, and main algorithms of text mining and their potential applications.

Text analysis: Text analysis, also called text mining, is the process of extracting values ​​from large amounts of unstructured text data. It can be used in various ways, such as information retrieval, pattern recognition, markup and annotation, information extraction, emotion evaluation, prediction analysis. Emotional analysis: Emotional analysis, also called opinion mining, is designed to extract subjective opinions and emotions from text, video, or audio data. The basic goal is to determine the attitude of an individual or group against a specific topic or overall situation. Use it when you want to know what stakeholders are saying.

Text mining, also called text data mining, is roughly equivalent to text analysis and is the process of obtaining high quality information from text. High quality information is usually derived from design patterns and trends through statistical model learning. Text mining is usually done by constructing input text (usually parsing, adding derived language features, deleting other functions, inserting databases), exporting schemas to structured data, and evaluating and interpreting the final output The process is accompanied. "High quality" in text mining usually refers to association, novelty and interesting combination. Typical text mining tasks include text classification, text clustering, concept / entity extraction, granularity generation, sentiment analysis, document summary, entity relationship modeling (learning relationships between named entities).