This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Postgraduate Handbook 2025-2026 (archived)
Module ACCT42615: Natural Language Processing & Textual Analytics
Department: Accounting
ACCT42615: Natural Language Processing & Textual Analytics
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 4 | 15 | Not available in 2025/2026 | None. | Durham |
Tied To
- L1T509: Accounting
- L1T709: Accounting Analytics & Sustainability (MAAS)
- L1T712: Accounting Analytics & Sustainability (DAAS)
- L1T714: Accounting Analytics & Sustainability (CAAS)
- N4R201: Accounting (with Integrated Studies)
Pre-requisites
- None
Corequisites
- None
Excluded Combinations
- None
Aims
- This module will aim to introduce students to the concepts, text mining, natural language processing and textual analytics. In particular, on the successful completion of this module students will be able to:
- identify the potential of text analytics for developing academic research in accounting and audit;
- apply text analytics software to organize, analyse and interpret textual data;
- use of document text mining for audit.
Content
- Introduction to Text Mining.
- Data extracting and pre-processing for Text Mining.
- Basics to Python Language.
- Text Analytics techniques.
- Natural language Processing (NLP) methods.
- Probability and regular expressions.
- Text modelling and machine learning.
Learning Outcomes
Subject-specific Knowledge
- By the end of the module students should be able to show:
- clear understanding of the text utilisation in accounting and audit research;
- identification of different applications of Natural Language Processing (NLP);
- understanding of readability and regular expression concepts;
- development of clear understanding of XBRL and its applications;
- demonstration of advanced knowledge and understanding of the implementation of text machine learning techniques to resolve accounting and audit problems and challenges.
Subject-specific Skills
- By the end of the module students should be:
- competent in text mining and capture from a variety of data sources;
- competent in integrating text in a NLP model;
- capable of implementing different text machine learning and AI tools.
Key Skills
- Text coding and programming skills.
- The ability to communicate effectively: communicating complex ideas.
- The ability to think critically and creatively and to argue coherently.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- The module is delivered via online learning, divided up into study weeks with specially produced resources within each week. Resources vary according to the learning outcomes but normally include: video content, directed reading, reflective activities, opportunities for self-assessment and live scheduled webinars. The hours as depicted in the Teaching Methods and Learning Hours table are indicative.
- The formative assessment serves to encourage students to study regularly and to monitor their learning progress. Tutors provide feedback on formative work and are available for individual consultation as necessary (usually by email and Zoom or Microsoft Teams).
- The summative assessment of the module is designed to test the acquisition and articulation of knowledge and critical understanding, and skills of application and interpretation within the accounting and audit context.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Online Learning Activities | 90 | ||||
| Independent Study | 60 | ||||
| Total | 150 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Individual assignment | 100% | Assignment | 3000 words max or equivalent | 100% |
Formative Assessment
Students undertake a series of activities aligned to the module content, receiving ongoing feedback on the theoretical knowledge and how it is applied.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.