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
Eric has about 15 years of analytics and data science experience in the financial services, start-ups and web analytics. Prior joining ISS, he was an Enterprise Data Scientist with Thomson Reuters and was also director of quantitative data science in a China fintech start-up with 4 million users....
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...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.