MS4H05 - Text Mining and Natural Language Processing 04 Dec 2020 - 31 Aug 2026 | Version 1
Associated Module Information
| Module Code: | MS4H05 | ||
|---|---|---|---|
| Module Title: | Text Mining and Natural Language Processing | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematics | ||
| Faculty Sub Group: | Computing | ||
| Module Leader: | Samuel Jobbins | ||
| Module Team: | Stephanie Perkins, Rebecca Peters | ||
| First Intended Intake: | JAN 2025 | Final Year of Intake: | 2025 |
| Date Closed: | |||
| Credit Value: | 10 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100359 - artificial intelligence | 100992 - machine learning | 101034 - statistical modelling |
| HECOS Code Weighting: | 25 | 50 | 25 |
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 04 Dec 2020 |
| Valid To | 31 Aug 2026 |
Module Aims
To analyse unstructured data in the form of text including basic descriptive tasks, sentiment analysis and topic analysis to identify trends and patterns.
To apply a range of natural language algorithms to critically evaluate patterns and relationships from text.
Content Summary
Introduction to the concepts of text mining including natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications.
Case studies on text classification, topic modelling, sentiment analysis and social media mining.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 8 |
| Seminar | 8 |
| Tutorial | 8 |
| Independent Study | 40 |
| Directed Study | 36 |
| Total Hours Selected | 100 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To understand the methods for analysing unstructured data and explain the wider context of their value in Data/AI. |
| LO2 | To utilise and critically evaluate a variety of NLP algorithms to assess and interpret real-world complex unstructured data. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Set Exercise - Not Time Constrained (CW) | Set Tasks - not-time constrained 1 | Within a software package, select and apply a range of algorithms | 0 | 3000 | 70 | No | 40 |
| Portfolio | Portfolio 1 | Practical demonstration of code | 10 | N/A | 30 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Set Tasks - not-time constrained 1 | ✔ | ✔ | |
| Portfolio 1 | ✔ | ✔ | |