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 feedback 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 feedback 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 feedback, reviews or comments to business and IT professionals.
This course is part of the Artificial Intelligence, Graduate Certificate in Business Analytics Practice and Graduate Certificate in Practice Language Processing Series offered by NUS-ISS.
This course need only be taken once throughout the Stackable Programme in Data Science.