Term 2 - Online
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Text data is a primary form of data and its analysis can provide important insights into the behaviours and needs of individuals and organisations. This subject will introduce students to methods for analyzing text and unstructured data.
The following topics will be covered: introduction to text analytics and distinctive features of text data; text data acquisition and storage; text representations and transforming text for analysis; similarity and clustering for text analysis dimensionality reduction strategies; topic and thematic analysis; text classification; text analytics for information extraction and named entity recognition; multi‐lingual text data; applications of text analytics: question answering, essay grading and sentiment analysis; case studies: clinical notes, learning management systems.
Intended learning outcomes
On completion of this subject, students should be able to:
- Evaluate and apply key analytics techniques used in natural language processing and text retrieval and deploy them in combination for different scenarios in text analysis
- Critique component technologies in commonly deployed systems that analyse text and be able to communicate issues relevant to the effective implementation and operation of such systems
- Explain and justify to others the use of text analytics algorithms for real world use by individuals or organisations
Students will be provided with the opportunity to practice and reinforce:
- High level written communication skills
- Advanced information and interpretation skills
- Advanced analytic, integration and problem‐solving skills
- Demonstrate competence in critical and theoretical thinking through report writing and online discussions
Last updated: 10 November 2019