Singapore University of Social Sciences

Natural Language Processing

Natural Language Processing (AIB551)

Applications Open: 01 May 2024

Applications Close: 15 June 2024

Next Available Intake: July 2024

Course Types: Modular Graduate Course

Language: English

Duration: 6 months

Fees: $2200 View More Details on Fees

Area of Interest: Business Administration

Schemes: Alumni Continuing Education (ACE)

Funding: To be confirmed

School/Department: School of Business


This course AIB551 Natural Language Processing provides an introduction to novel and effective machine learning techniques to understand and analyse human language for business applications. The module will explain the key approaches of acquiring and handling natural textual information using Python. Students will learn to pre-process and transform unstructured/semi-structured textual data into analysable format. Next, students will learn advanced data science solutions created for Natural Language Processing (NLP) including word, sentence and document embeddings, word clouds, Named Entity Recognition (NER), sentiment analysis, topic modelling, and influencer/topic network analysis enabled by supervised, unsupervised and deep machine learning techniques. Real-world examples, hands-on class exercises and assignments will be designed to help students to prepare and analyse data by harnessing Python’s libraries and using NLP techniques to extract new knowledge being integrated into data analysis workflows and improve business performance.

Level: 5
Credit Units: 5
Presentation Pattern: Every semester


  • Basics of Natural Language Processing (NLP)
  • Build NLP Vocabulary Using Python
  • Bag-of-Words (BoW) and TF-IDF Vectors
  • Pre-trained Word2vec Model
  • Venture into Doc2vec, Sent2Vec and Universal Sentence Encoder
  • Word Cloud Analysis
  • Standard and Customised Sentiment Analyser
  • Entity Recognition Techniques
  • Evaluation Metrics of Classifier
  • Topic Modelling Techniques
  • Topic and Influencer Network
  • State of the Art in NLP - Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

Learning Outcome

  • Develop know-how of the applications of AI tools and technologies in areas of NLP
  • Assess the possibilities and implications of NLP in different industries
  • Formulate NLP analytics strategies
  • Prepare unstructured/semi-structured data into analysable format
  • Design NLP solutions through supervised, unsupervised and deep machine learning techniques
  • Revise data analysis workflows by integrating NLP solutions to improve business performance
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