Actionable Insights for Effective Customer Experience Management

Actionable Insights for Effective Customer Experience Management
Understanding and extracting key insights from customer data is critical for enterprises and organisations to manage and improve the experience for their customers.
Customers constantly interact and use different brands, products and services.
Funding Body
Award Value
Start Date
End Date
Duration
Funding Body
Innovate UK and Sentisum
Award Value
£169,298
Start Date
February 2017
End Date
January 2019
Duration
2 years

With the advent of the internet and social media, large volumes of customer experience data is generated on a day-to-day basis. Understanding and extracting key insights from this data is critical for enterprises and organizations to manage and improve the experience for their customers.

The aim of this project is to apply machine learning (ML) and natural language processing (NLP) in order to extract the key entities (referred to as aspects) and the sentiment expressed towards them to better understand the emotion of the customer.

The novelty of the project comes from developing innovative state-of-the-art methods to accurately identify the entities, contexts related to the entities and also the sentiment about them. In particular, we are exploring how ML models can be influenced by background knowledge extracted from dependency parsers and generative lexicons. Our recent work suggests that effective ways to discover the context in which a sentiment is being expressed towards the aspects is crucial to improving the accuracy of sentiment analysis.