Text Mining and Social Network Analytics

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• apply analytical techniques to analyse text and network data;
• derive actionable insights through the results of analyses.
• evaluate suitable approaches for a range of analytical tasks related to text mining and social network analytics;
• collect, integrate and prepare text and social network data with other types of data collected from various sources;

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• how various analytical techniques can be used to uncover the potential of text and network data to gain actionable insights and support marketing decisions.
• the complexities of collecting, integrating, processing and managing text and network data from a wide range of internal and external sources;
• some common text mining and social network analytics activities in contemporary organisations;

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• communicate ideas and arguments fluently and effectively through a written report;
• use computing and IT resources effectively;
• demonstrate confidence in your own ability to learn new concepts.
• manage yourself, time and resources effectively;

The topics covered include:

• Sources of text and social network data;
• Techniques to pre-processing text and social network data;
• The key metrics used in social network analytics (e.g. degree centrality, betweenness centrality, closeness centrality, etc.);
• Classification with social network techniques; Non-relational classifier, relational classifier, collective inference procedure;
• Approaches for sentiment analysis: natural language processing, pattern-based, machine learning algorithm;
• Applying text mining and social network techniques in social media analytics;
• Popular measurements/metrics used in social media analytics.

The precise topics covered may change slightly in response to what is determined to be the most relevant based on academic and industry practice.

Teaching and learning methods

Teaching methods include:

• Lectures exploring the problems and concepts
• Discussion sessions
• Guided independent study

Learning activities include:

• An assignment
• Laboratory work
• Case study/problem solving activities
• Private study

Resources & Reading list

General Resources

Access to journal articles. Library support

Computer labs equipped with R/SAS software. Software

Textbooks

Miner, G. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Academic Press.

Aggarwal, C.C. (2011). Social Network Data Analytics. Springer.

Zafarani, R., Abbasi, M.A., Liu, H. (2014). Social Media Mining: An Introduction. Cambridge University Press.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External