Sentiment Analysis in Social Media
Date: To be advised
Duration: 1 Day
Course Overview
Sentiment analysis or opinion mining is concerned with extracting opinionated content, and analysing it to provide some clue on how users feel about an issue/thing/service/product, and so on. There are currently many packages in different programming languages (e.g. R, Python) for mining data from social media sites. This makes it easy for anyone (data analysts, executives) to obtain social media data for garnering insights into behaviours and sentiments. The ability to handle and make use of unstructured data would prove useful. This course covers the concepts and tools useful for performing analysis on social media data such as tweets and reviews. Participants will be able to perform text analytics, sentiment analysis, as well as social network analysis.
This course is part of Professional Certificate in Digital Marketing and Customer Insights.
Learning Outcomes
The course will equip learners with the following competencies:
1. provide participants with both the theoretical foundations and coding foundations in sentiment analysis applications
2. provide participants with sufficient machine learning foundations to build sentiment analysis models
3. equip participants with the skills to mine data from the social media to build useful AI applications for gathering and deriving insights
4. cover examples how social media data and sentiment analysis can be used to gather business insights and perform market research
Tools
R and R Studio
Who Should Attend
Social media executives and marketing managers.
Prerequisites
Familiarity with R programming language and control structures
Facilitator
(Click photo to view biography)
Dr Lek Hsiang Hui
Dr Lek Hsiang Hui
Dr Lek Hsiang Hui is a Specialist in Data Analytics, System Analysis and Development. He is an IT techie who is passionate about computer systems and technology.
He is involved in a few startups and is constantly looking into innovative IT solutions which can be translated to business ideas, with the recent one in the area of Big Data Analytics. He is also a mobile application developer and has produced more than 10 mobile apps.
Dr Lek has been teaching various undergraduate courses and executive courses in National University of Singapore (NUS) since 2006. Some of these modules include programming methodology, enterprise system analysis and development, and data mining. During this period, he has won a number of teaching awards such as NUS Annual Teaching Excellence Award (2015/16, 2016/17, 2017/18) and NUS Annual Teaching Excellence Honor Roll (2018/19), Faculty Teaching Excellence Award (2014/15, 2015/16, 2016/17), and Faculty Teaching Excellence Award Honor Roll (2017/18).
Dr Lek received his Doctor of Philosophy (Information Systems) from NUS in 2013. His research area is in Natural Language Processing (Sentiment Analysis). He graduated with a Bachelor’s Degree (1st class Honors) in Computer Engineering from NUS.
Industry Credentials
Shopping Malls (UOL, Marina Square, ION), Healthcare (SingHealth), Government (STB), various SMEs in F&B, Apparels, Cleaning Solutions, Photography, Health Products, Baby Products
What Our Participants Say
“Interesting course. Great lecturer!”
– Lin Wei Hong Benjamin
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Code
TGS-2020504965
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older