Case study: Natural language processing for sentiment analysis


Pain Points

Inability to manually parse useful information from a massive set of customer reviews


Natural language processing to perform sentiment analyses of customer-derived text data

Result/ Benefits

Able to quickly harness insights from large volume of text information

Pain points

Our clients operate in industries where their products and services are regularly reviewed on social media platform. In addition, they receive countless feedback through e-mails and calls.

Due to the volume of these customer reviews, e-mails and calls, our clients found it difficult analysing them to understand the public’s perception of their business, thereby limiting the usefulness of the data to guide business decisions.


Using natural language processing (“NLP”) techniques, we analysed large volume of unstructured text information our clients’ customers provided.

This analysis converted each piece of information into numeric scores based on how positive they were. Additionally, it identified key trending topics associated with their customers’ feedback.


Numerical ratings directly provided by consumers can be unreliable as there is a human tendency to overrate. Using NLP techniques, our clients can form a better understanding of the true sentiments felt by their customers over time.

It is practically difficult for a person to read through each text information and extract useful insights. The use of NLP helps our clients to quickly harness insights from large volume of text information to drive business decisions. For example, our solution can identify constructive criticisms from our clients' customers which in turn helps our clients to focus their efforts on addressing key issues.



Our Data Science team, comprising a group of specialists with multi-disciplinary skills, helps our clients make better strategic and operational decisions by turning data into actionable insights.

For more information, contact:

Adrian Tan
Partner and Industry Lead for Technology, Media and Telecommunications
T +65 6594 7876
[email protected]