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The University of Southampton

MANG6331 Text Mining and Social Network Analytics

Module Overview

There is a growing number of data in text or network format. These data provide a rich source of information for contemporary organisations to understand customers. The aim of this module is to introduce some popular techniques and tools used to conduct analysis on these text or network data. After studying this module, students will develop a conceptual understanding of how to use text or social network data to support and improve marketing activities.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

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

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

Having successfully completed this module you will be able to:

  • collect, integrate and prepare text and social network data with other types of data collected from various sources;
  • apply analytical techniques to analyse text and network data;
  • evaluate suitable approaches for a range of analytical tasks related to text mining and social network analytics;
  • derive actionable insights through the results of analyses.
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;
  • manage yourself, time and resources effectively;
  • use computing and IT resources effectively;
  • demonstrate confidence in your own ability to learn new concepts.


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.

Learning and Teaching

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

Independent Study63
Total study time75

Resources & Reading list

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

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

Computer labs equipped with R/SAS software. Software

Access to journal articles. Library support

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



Computer practicals


MethodPercentage contribution
Coursework  (2000 words) 100%


MethodPercentage contribution
Coursework  (2000 words) 100%


MethodPercentage contribution
Coursework  (2000 words) 100%

Repeat Information

Repeat type: Internal & External


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:


Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the mandatory/additional reading text as appropriate.

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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