Text and data analysis

Resource 1

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

Resource 2

Organisations today work with vast quantities of unstructured textual information – from email and social media engagements to web server logs and call-centre notes. Across industries, there is a strong need for companies to analyse this text and make it quantifiable, in order to generate insights, respond to trends, and remain competitive. The Data Science: Text Analysis Using R online certificate course provides a comprehensive, practical grounding in the process of textual data mining. Guided by industry expert Professor Kenneth Benoit, you’ll learn how to conduct a text analysis from start to finish, including preparing raw text, unpacking and categorising it, and evaluating the final analytics using R programming language. You’ll also learn how to effectively use Quanteda – an online library for the quantitative analysis of textual data, developed by Professor Benoit.

Resource 3

From social media to news articles to machine logs, text data is everywhere. This class will teach you about Information Extraction: how to extract structured data from text in order to derive valuable insights. You will learn about information extraction applications in various domains, such as social media, healthcare analytics, and financial risk analysis. You will explore common text analytics tasks, including entity, relation, and event extraction, as well as sentiment analysis. Finally, you will dive into "Declarative Information Extraction", a powerful method for doing high-performance and high-quality text analytics, and gain hands-on experience writing your own extractors