%0 Conference Proceedings
%T An Overview of the Application of Sentiment Analysis in Quality Function Deployment
%+ Stellenbosch University
%A Sarema, Blessed
%A Matope, Stephen
%Z Part 8: Customer Behavior and E-business
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 20th Conference on e-Business, e-Services and e-Society (I3E)
%C Galway, Ireland
%Y Denis Dennehy
%Y Anastasia Griva
%Y Nancy Pouloudi
%Y Yogesh K. Dwivedi
%Y Ilias Pappas
%Y Matti Mäntymäki
%I Springer International Publishing
%3 Responsible AI and Analytics for an Ethical and Inclusive Digitized Society
%V LNCS-12896
%P 519-531
%8 2021-09-01
%D 2021
%R 10.1007/978-3-030-85447-8_43
%K Sentiment analysis
%K Quality Function Deployment
%K Opinion Mining
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Customer feedback is important in continuous improvement of products and services for businesses to stay ahead of competition. With the advent of the internet, online product reviews from various platforms attracts a lot of comments from customers as they share their sentiments from experiences with the different products and services. The sentiments shared online are an important resource for mining opinions that can help businesses to improve on their products and services. Online sentiment analysis has brought about endless possibilities to incorporate these sentiments in product development in continuous improvement. However, most sentiments analysis research done seem to muzzle the customer comments into three points of view that are positive, neutral and negative. Though this approach is excellent in providing a general perception of customers about a particular product or service, it falls short in outlining specific product features that may require improvement in order to increase customer satisfaction from the Quality Function Deployment (QFD) perspective. This paper presents an overview of how sentiment analysis has been applied to a number of products and services reviews. The aim being to highlight the gaps in sentiment analysis output data and how QFD can be integrated with sentiment analysis to make the sentiments valuable. An integration conceptual framework is proposed to stimulate research into the area.
%G English
%Z TC 6
%Z WG 6.11
%2 https://inria.hal.science/hal-03648146/document
%2 https://inria.hal.science/hal-03648146/file/512902_1_En_43_Chapter.pdf
%L hal-03648146
%U https://inria.hal.science/hal-03648146
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-11
%~ IFIP-I3E
%~ IFIP-LNCS-12896