**1. Introduction**

Traditional machine learning algorithms experience the data and learn the hypothesis. Tree and rule-based algorithms learn the hypothesis using the attribute-value pairs from the input data. Machine cannot go beyond the task of identifying features and opinions from the reviews as it never possess prior knowledge to understand the relationships among the attributes and context specific constraints that are available among the product features and opinions.

Semantic web ontology helps to overcome this problem. Ontology [1] encodes the relationships among the concepts of features and opinions with inequality constraints, semantic

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

characteristics, and cardinality restrictions. This ontology is used as background knowledge on the product reviews. The knowledge mined from the ontology is expressed in the form of semantic rules. These semantic rules emphasize the target sentiment expressed on the product feature. Machines are able to classify the product reviews automatically with exact sentiments learned on the product feature.

reviews among the category of products are useful to the customer in personalized recommendations. These feature-level sentiments are aggregated to form the product sentiment. Chen and Wang proposed [10] a novel explanation interface that fuses the feature sentiment information into the recommendation content. They also provided the support for multiple products comparison with respect to similarity using the common feature sentiments. Gurini et al. proposed [11] friends recommendation technique in Twitter using a novel weighting function which is called sentiment-volume-objectivity (SVO) that considers both the user interests and sentiments. Xiu et al. proposed [12] a recommender system that recognizes the sentiment expressions from the reviews, quantified with the sentiment strength and appropriately recommend products according to customer needs. Recently, Dong et al. developed [13] a product recommendation

Sentiment-Based Semantic Rule Learning for Improved Product Recommendations

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The utilization of ontologies for better product recommendations is an emerging research area. Uzun and Christian developed [14] a semantic extension to FOKUS recommender system. This extension is capable of integrating contextual and semantic information in the recommendations. Hadi Khosravi and Mohamad Ali introduced [15] a semantic recommendation procedure using ontology on online products based on the usage patterns of the customers. The works on ontology-based recommender systems [14, 15] was neither concentrated on utilizing the depth information of the domain feature nodes from the ontology tree nor on height of the ontology tree. These properties act as supervised weights in improving the sentiment of

strategy that combines both similarity and sentiments to suggest products.

the feature and thereby help in improving the recommendations.

constraints and non-taxonomical constraints are presented below.

**EXTRACT\_TAXONOMICAL\_CONSTRAINT (Ontology)**

for each concept with hierarchy from Ontology

with the product information from the ontology.

**Output: machine interpretable rule {A**➔**B}**

contentconstraint = false;

**Input: PROO {Ontology}**

{

{

**3. Improving product recommendations using semantic sentiments**

The recommender system proposed in this work is a knowledge-based recommender system that encapsulates the product catalog knowledge in the form of classes in the ontology and product functional knowledge in the form of facts in the ontology. The user profile is created as and when the user navigates the web pages for the products. The user profile is indexed

The principal objective of recommending products using sentiments learned from the ontology is to utilize the taxonomical and non-taxonomical constraints mined from ontology for sentiments. The detailed procedures expressed in algorithmic form for learning taxonomical

Sentiment analysis [2] plays a vital role in understanding the opinions from online reviews. It helps to understand the views of the people on the product, to take quick purchase decisions on the product, and to improve the availability of the product in the market. Online reviews affect the emotion of the readers. Measuring the effect of the sentiment on the semantic rules in the form of knowledge spread is performed to understand whether positive reviews of the product spread faster than negative reviews. The kind of emotions that are more representative on various e-commerce sites about the product is also well identified. Furthermore, the type of sentiment expressed in reviews based on temporal changes on the features of the product is determined in a proper manner.
