Sentiment-Based Semantic Rule Learning for Improved Product Recommendations

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 relationship among the product features ‘as-a-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

Analysis of sentiment communities in online networks

This article reports our experience in developing a recommender system (RS) able to suggest relevant people to the target user. Such a RS relies on a user profile represented as a set of weighted concepts related to the user's interests. The weighting function, we named sentiment-volume-objectivity (SVO) function, takes into account not only the user's sentiment toward his/her interests, but also the volume and objectivity of related contents. A clustering technique based on modularity optimization enables us to identify the latent sentiment communities. A preliminary experimental evaluation on real-world datasets from Twitter shows the benefits of the proposed approach and allows us to make some considerations about the detected communities