NICF- Text Analytics

NUS Institute of Systems Science

How long?

  • 3 days
  • in person

What are the topics?

NUS Institute of Systems Science

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Who should attend

This course is designed for both Business analytics and non-business analytics professionals. These include:

  • Business and IT professionals seeking analytical skills to handle large amounts of textual data (e.g. customer feedbacks, product reviews on social media, etc.) for insights to improve business process and decision making
  • Individuals who have no knowledge or experience in text analytics and would like to gain some knowledge in this area so that they may explore work opportunities in business analytics
  • Data analysts, business users and IT professionals who want to move from the structured data to large amounts of unstructured, text data

Prerequisite

  • Participants must successfully completed NICF- Predictive Analytics - Insights of Trends and Irregularities (SF) course offered by NUS-ISS.
  • Participants must be competent in R programming skills, and must also be familiar with RStudio.
  • Participants must be experienced in package installation and able to follow given instructions to install required packages on their laptop before class

About the course

Do you know how to analyse customer sentiments about your company, products and services? Or how to keep track of your company’s service & quality delivery so that you are able to act quickly to insights that drive your business?

About 80% of enterprise-relevant data is in unstructured or semi-structured format. These include emails, documents, surveys, feedback forms, warranty claims, contact-centre notes and transcripts, web pages, news, data from social media, audios, videos and many more. A predominant amount of such data is available as text.

In order to gain competitive edge in the market, businesses and organisations are finding a growing need to expand their analysis scope to cover text data, especially in regards to customer feedbacks and social media data. This is so that critical insights can be uncovered to support business decision making and process improvement.

This course aims to equip you with the knowledge and skills to effectively analyse large amounts of textual data such as customer feedbacks and social media conversations to discover themes, patterns and trends to aid in improving business process and decision making. In scenario-based case studies, you will be introduced to common text analytics tasks such as data pre-processing and preparation, linguistic/knowledge resources management, concept extraction, text categorisation, clustering, association and trend analysis. You will practise performing these tasks following a well-defined process in hands-on sessions.

This 3-day data mining course focuses on introducing the essential analytical skills in modelling unstructured textual data such as customer feedbacks, reviews or comments to business and IT professionals.

Key Takeaways

At the end of the course, participants will be able to:

  • Identify main themes or topics in the collection of documents or textual data (e.g. the prominent issues customers are complaining about).
  • Discover relationships and patterns among topics (e.g. which issues tend to co-occur in complaints).
  • Categorise documents based on discovered topics and user-definable criteria, such as grouping complaints about similar issues for further investigation.
  • Perform sentiment analysis on customers’ comments, reviews, or other forms of opinions to gain a good sense about how customers feel about their company, products and services.
  • Extract useful information from text as structured data to enable integration into the traditional data mining process.
  • Incorporate business understanding and domain knowledge into the analysis through lexical and knowledge resources.
  • Perform the above tasks using the open-source language

What Will Be Covered

At the end of the course, participants will be able to:

  • Identify main themes or topics in the collection of documents or textual data (e.g. the prominent issues customers are complaining about).
  • Discover relationships and patterns among topics (e.g. which issues tend to co-occur in complaints).
  • Categorise documents based on discovered topics and user-definable criteria, such as grouping complaints about similar issues for further investigation.
  • Perform sentiment analysis on customers’ comments, reviews, or other forms of opinions to gain a good sense about how customers feel about their company, products and services.
  • Extract useful information from text as structured data to enable integration into the traditional data mining process.
  • Incorporate business understanding and domain knowledge into the analysis through lexical and knowledge resources.
  • Perform the above tasks using the open-source language, R

Experts

Fan Zhen Zhen

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 ...

Leong Mun Kew

Mun Kew is appointed as Director, Graduate Programmes of ISS. Prior to joining ISS, Mun Kew held multiple positions with the National Library Board, Singapore, as CTO, Deputy CIO & Director CIO Office, and Director, Digital & Knowledge Infrastructure Division. As Deputy CIO, he was respon...

Wang Aobo

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...

NICF- Text Analytics at NUS Institute of Systems Science

From  SGD 2 889$2,238
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