Course Content
- Introduction to Sentiment Analysis: Discover the fundamentals of sentiment analysis and its applications across various social domains.
- Deep Neural Networks for Feature Learning: Harness the power of embeddings like word2vec, GloVe, and GenAI to learn features using deep neural networks.
- Entity and Aspect Extraction: Extract entities and aspects from text, focusing on products, brands, features, and attributes.
- Advanced Sentiment Mining Techniques: Apply different approaches for sentiment mining, including supervised learning, deep learning, and zero-shot learning with Large Language Models (LLMs).
- Model Evaluation and Comparison: Evaluate and compare the performance of various sentiment mining models and methods.
- Sentiment Visualisation Tools: Learn to visualise sentiment analysis results effectively.
- Hands-on Experience: Gain practical experience with popular frameworks and libraries such as TensorFlow, PyTorch, HuggingFace Transformers, and spaCy. Work with real-world datasets and case studies from IMDb, Amazon, Yelp, and Twitter.
This course is part of the
Artificial Intelligence and
Graduate Certificate in Practical Language Processing Series offered by NUS-ISS.
Key Takeaways
- Identify Sentiment Analysis Opportunities: Understand where sentiment analysis can be effectively applied across different domains.
- Practical Application: Apply sentiment mining techniques in marketing, customer service, product development, and social media analysis.
- Model Development and Deployment: Build and deploy your own sentiment mining models and systems using state-of-the-art technologies and Generative AI tools.
- Granular Sentiment Evaluation: Evaluate and assess sentiment analysis at a detailed level for specific entities and aspects.
- Advanced Skills Enhancement: Enhance your expertise in the rapidly evolving field of sentiment mining using deep learning and Large Language Models (LLMs).
Course Logistics
- No Printed Materials: Course materials are accessed digitally. Do kindly note that no printed copies of course materials will be issued.
- Device Requirements: Bring an internet-enabled device (laptop, tablet, etc) with power chargers to access and download course materials.
If you are bringing a laptop, kindly refer to the table below for the recommended tech specs:
|
|
Minimum
|
Recommended
|
|
Operating Systems
|
• Windows 7 above
• Mac OS
|
Laptop running the latest
version of either Windows or
Mac OS
|
|
System Type
|
32-bit
|
64-bit
|
|
Memory
|
8 GB RAM
|
16+ GB RAM
|
|
Hard Drive
|
256 GB disk size
|
|
|
Others
|
• An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out) |
DirectX 10 graphics card for graphics hardware acceleration
|
Join Us
Elevate your data analysis capabilities. Register now to discover sentiment insights from text data.
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
Find out more.
WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.