Sentiment-Based Semantic Rule Learning for Improved Product Recommendations [chapter]

Dandibhotla Teja Santosh, Bulusu Vishnu Vardhan
2018 Machine Learning - Advanced Techniques and Emerging Applications  
Crucial data like product features and opinions that are obtained from consumer online reviews are annotated with the concepts of product review opinion ontology (PROO). The ontology with instance data serves as background knowledge to learn rule-based sentiments that are expressed on product features. These semantic rules are learned on both taxonomical and nontaxonomical relations available in PROO ontology. These rule-based sentiments provide important information of utilizing the
more » ... p among the product features 'asa-unit' to improve the sentiments of the parent features. These parent features are present at the higher level near the root of the ontology. The sentiments of the related product features are also improved. This approach improves the sentiments of the parent features and the related features that eventually improve the aggregated sentiment of the product. The result is either the change in the position of the product in the list of similar products recommended or appears in the recommended list. This helps the user to make correct purchase decisions.
doi:10.5772/intechopen.72514 fatcat:qlsvcr33dzgqjepsoixcbkzj7y