This course equips students with the expertise to analyze and extract valuable insights from unstructured textual data. Emphasizing practical applications, the course explores key text mining techniques such as natural language processing, sentiment analysis, and topic modeling to address real-world business challenges. Through hands-on experience with modern tools and methodologies, students will learn to leverage textual data effectively to support strategic decision-making across diverse business domains.

The course will be delivered in Semester 2, comprising six 2-hour lectures and six 1-hour workshops/tutorials, totalling 18 hours of contact time. Lectures will introduce key concepts, theories, and methodologies in text mining. The workshops/tutorials will provide hands-on, formative activities where students apply these concepts to real-world datasets using text mining tools and techniques.

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The summative assessment is an individually assessed project. Students will receive a briefing document along with associated datasets and are required to perform text mining to address the questions outlined in the brief. Students must submit a project report (maximum 15 pages, excluding references and appendices) along with their programming source code. The assessment will be evaluated against criteria structured around the intended learning outcomes. Detailed marking guidance and grade descriptors will be provided in advance.

This course aims to develop students' ability to analyse unstructured textual data and extract meaningful insights to address contemporary business challenges. It provides a robust understanding of key text mining techniques and their application across a range of business contexts. The course also aims to build practical skills in applying text analytics tools and to foster critical awareness of the ethical considerations and limitations involved in using text mining to inform business decision-making.

By the end of this course, students will be able to: 

1. Critically evaluate text mining principles and techniques in addressing business challenges.

2. Apply text preprocessing techniques to prepare textual data for analysis.

3. Utilize text mining techniques, such as sentiment analysis and topic modelling, to extract actionable insights.

4. Analyse and visualize textual data to identify patterns, trends, and opportunities for business decision-making.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.