Comprehensive course analysis
Who should attend
This is an intermediate course, suitable for professionals with an interest or requirement to understand digital marketing and social engagement for customers.
It is applicable for professionals engaged in the following areas.
- Customer analysts performing deeper analytics on sentiment analysis on customer feedbacks and reviews
- Data scientists in financial services doing applied sentiment mining for applications in finance including fraud, trading.
- Data analysts in the financial services who use internal research and external news for research.
- Analysts who want to automate and extract insights from the voluminous internal and external textual documents in their organisation
About the course
Do you have a lot of textual data from various sources like customers, internal documents, emails, news articles and the social media that comes in fast and furious? Do you want to extract meaningful opinions and sentiments from these textual data automatically? This course further extends the knowledge and skillsets built by the NICF - Text Analytics course. It equips the attendee with the necessary skillsets to design sentiment analysis system and apply them in various social fields.
The ability to process and analyse voluminous textual data provides the participants an edge in this new media age. The objective of this course is to introduce participants to sentiment analysis and its applications. Participants will gain the requisite skills to evaluate the supervised learning algorithms for sentiment classification. They will be able to evaluate and analyse granular meaning of texts from documents and articles.
The course will assume that the participants have good knowledge of text analytics and techniques, and some hands on experience of modelling using these techniques using Python. Participants are also expected to have knowledge of statistics at the level of the NICF - Statistics Bootcamp course.
At the end of the course, the participants will be able to:
- Identify where sentiment analysis can be applied
- Evaluate and analyse the classification techniques for sentiment classification and apply it with open source libraries
- Design a sentiment analysis system for customer feedback and reviews
- Design a sentiment analysis system for news and social media for applications in finance
- Evaluate and assess sentiment analysis at a granular level for entities and aspects
What Will Be Covered
- Introduction to sentiment analysis and its applications in various social domains.
- Overview of related tasks of NLP to sentiment analysis
- Supervised learning classification algorithms for sentiment analysis
- Entity and aspect mining for sentiment analysis
- Sentiment visualization tools
- Applications of sentiment analysis to customer analytics and financial applications
- Sentiment analysis and its psychological basis
Zhenzhen has been with Institute of Systems Science, NUS, since 2006. She currently lectures in the Master of Technology programme in the areas of case-based reasoning, text mining, KBS development, hybrid KBS, and formal specification. Prior to joining ISS, she was a senior research engineer at ...
Aobo Wang is currently a lecturer and consultant with National University of Singapore, Institute of Systems Science (NUS-ISS) with responsibility in teaching, consulting and research. He lectures in the areas of text mining, natural language processing, and machine learning. He received his Ph.D...
Experience National University of Singapore Full-time, Nov 2020 – Present Siemens Advanta Consulting Data Scientist, Jan 2019 – Nov 2020 Dyson Data Insights Analyst Company NameDyson, May 2017 – Jan 2019 IHG Hotels & Resorts Revenue Analytics Intern, Aug 2016 – Mar 2017 Hindustan...
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