Objective: A comprehensive feature extraction approach is specified by exploring the natural language rules for the extraction of various kinds of product features from Amazon online reviews. Method: The step-by-step feature extraction approach is followed to reach the goal of extracting maximum number of product features from the product reviews. Various types of nouns are extracted in the form of product features. These are namely frequent features, relevant features, implicit features and infrequent features. Findings: The results show that the comprehensive feature extraction approach performs better than the particular way for extracting the product features in the semantic environment. Applications: This approach is used in e-commerce websites to find out what product features are of interest to the customers. This model is useful in recommending products to the customers as the search for a product in the e-commerce site takes place, the features from the product reviews are helpful with the corresponding opinion orientations. This forms the basis for suggesting similar products using the calculated sentiments in the recommendation process.
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