Crucial data namely product aspects and opinions are extracted from online product reviews. The obtained opinions are further analyzed for orientations. These orientations that are positive, negative or neutral are counted to determine the sentiment of the aspect. The sentiments are often turned (unforeseen rise or fall) and due to this the quality of recommended products by the recommendation system is less. The purpose of this study is to assess the importance of aspects reputations in the similarity based product recommendations. A simulation model was established through the analysis of product reviews for ranking the aspects and identifying the frequent aspects among them. The case based reasoning of the searched product against the available similar products from the category are finally compared on the basis of aspect reputations. This comparison provides the list of sorted reputed products in the decreasing order of similarity as recommendations. Through this study, it was found that the recall measure calculated on the reputation based recommendations is better than sentiment based recommendations. The findings of this research are promising in terms of product recommendations using reputation.
product aspects, opinions, aspect rank, frequent aspects, aspect reputation, product similarity, product recommendations
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