Consumers often provide on-line reviews on products or services they have purchased, and frequently seek on-line reviews about a product or service before deciding whether to make a purchase. Organisations seek consumer opinions about their products, since this invaluable information allows them to improve future product versions, and to predict sales. The vast amount of on-line customer reviews has attracted research into approaches for intelligently mining these reviews to support decision-making processes. This chapter provides an overview of recent fuzzy-based approaches to sentiment analysis of customer reviews. It also presents a framework which can be utilised for sentiment analysis and review rating prediction tasks. The framework includes methods for preparing the dataset; extracting the best features for prediction via Singular Value Decomposition and a Genetic Algorithm; and constructing a classifier for performing the review rating predictions. © Springer International Publishing Switzerland 2016.
Neuro-fuzzy sentiment analysis for customer review rating prediction / Cosma, Georgina; Acampora, Giovanni. - 639:(2016), pp. 379-397. [10.1007/978-3-319-30319-2_15]
Neuro-fuzzy sentiment analysis for customer review rating prediction
Acampora Giovanni
2016
Abstract
Consumers often provide on-line reviews on products or services they have purchased, and frequently seek on-line reviews about a product or service before deciding whether to make a purchase. Organisations seek consumer opinions about their products, since this invaluable information allows them to improve future product versions, and to predict sales. The vast amount of on-line customer reviews has attracted research into approaches for intelligently mining these reviews to support decision-making processes. This chapter provides an overview of recent fuzzy-based approaches to sentiment analysis of customer reviews. It also presents a framework which can be utilised for sentiment analysis and review rating prediction tasks. The framework includes methods for preparing the dataset; extracting the best features for prediction via Singular Value Decomposition and a Genetic Algorithm; and constructing a classifier for performing the review rating predictions. © Springer International Publishing Switzerland 2016.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.