Introductory description

This module will introduce students to the exciting area of Text Analytics. The topics covered include (but are not limited to): Sentiment Analysis, Opinion Mining, Topic Modeling and Latent Dirichlet Allocation.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Information retrieval and classification.
Text preprocessing and information extraction.
Sentiment Analysis.
Topic modelling.
Communicating results.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate in depth knowledge of text analytics, preparation of executive reports, and practical applications.
• Given a business problem involving text analytics, to be able to choose appropriate techniques.

Indicative reading list

Modern Information Retrieval: The Concepts and Technology behind Search (ACM Press Books) 2010 by Dr Ricardo BaezaYates, Dr Berthier Ribeiro-Neto.

Subject specific skills

Process and prepare textual data for analysis.
Use textual data to improve predictive models.
Build and understand predictive models.
Compare and explain complex models.

Transferable skills

Be able to work within a team to analyse text data issues and propose solutions.
Be able to anticipate practical implications of working with different text mining techniques.