F21AA Applied Text Analytics

Dr Neamat El Gayar

Course co-ordinator(s): Dr Neamat El Gayar (Dubai).


The course aims to provide the students with knowledge and skills in applied text analytics focusing on Machine Learning
and Natural Language Processing tools.
In particular the course:
- Presents the area of text analytics and provides fundamental tools to extract, represent and analyse information from
text sources using machine learning models
- Provides a fundamental understanding of concepts and tools to build effective language aware systems and
- Presents basic understanding of deep learning models for Natural Language Processing applications and related
- Discusses current research advances, business cases and future direction of the field

Detailed Information

Course Description: Link to Official Course Descriptor.

Pre-requisites: none.

Location: Dubai.

Semester: 2.


• The following topics will be covered with varying levels of depth:
• Overview on ML models, techniques and use cases & ML project design.
• Language model & text processing principles
• Text classification & visualization
• Text Clustering & topic modeling
• Context-aware text analysis & n-gram model
• Chatbots
• Scaling text analytics
• A deep learning approach to NLP:
- Sequence models (ex: RNN, BRNN, LSTM ) & transfer learning
- Applications in Named Entity Recognition, learning word-embeddings, machine translation, sentiment classification
• Research Directions and Business Cases

Learning Outcomes: Subject Mastery

• Detailed understanding of the text analytics process and relevant applications and business values
• Ability to apply text analytic tools to work with unstructured text to reveal insights and uncover valuable information
• Understand challenges related to implementation and scalability
• Understand Deep learning approach to NLP problems and available tools for implementation
• Awareness of recent advances in the field of NLP & text analytics, relevant application and future directions in AI


Learning Outcomes: Personal Abilities

- Problem formulation, critical analysis and developing solution for practical problems
- Research skills, report writing and presentation skills
- Working in groups

Assessment Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22

SCQF Level: 11.

Credits: 15.