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

```
{
```
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

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

The recommender systems (RS) are the information filtering systems which deal with the large amount of information that is dynamically generated based on user's preferences, interests, and observed behaviors. These traditional recommender systems fall into three categories. They are collaborative filtering-based RS, content-based RS, and knowledge-based RS. The collaborative recommender systems are the most popular and widely implemented systems. These systems aggregate ratings from the set of users on the item and recommend it. It also identifies the users who are similar with the user from whom recommendations are to be provided. Resnick et al. developed [3] a system called GroupLens to help people to find articles they are most interested in. Stavrianou and Brun developed [4] an application to recommend products based on the opinions and suggestions written in the online product reviews. The content-based recommender systems learn the user profile based on the product feature where the user has targeted. Lang developed [5] a system called NewsWeeder which uses the words of the text as the features. Zhou and Luo developed [6] a content-based recommender system that views customer shopping history to recommend the similar products based on

The knowledge-based recommender systems provide the entity suggestions based on the deductions from user's needs and preferences. These systems have the knowledge about how a particular product meets the customer requirement based on the factual data. The user profile is also required to provide good product recommendations to the user. Case-based reasoning (CBR) is a kind of knowledge-based recommender system. Kolodner used [7] CBR to recommend the restaurants based on the user's choice of features. Burke used [8] the FindMe system to recommend the online products. Stefan et al. worked [9] on user log data to mine

Sentiment-based product recommendations have gained research importance in the recent times. The knowledge discovered in terms of product features and opinions from online product

the product preferences based on the like or dislike information available in the log.

sentiments learned on the product feature.

186 Machine Learning - Advanced Techniques and Emerging Applications

product is determined in a proper manner.

the similarity between the product features.

**2. Literature survey**

for each concept with hierarchy from Ontology

```
{
```
contentconstraint = false;

if(parent\_of(superconcept, subconcept))

```
contentconstraint = true;
         write(parent_of(superconcept, subconcept) ➔
target_class(subconcept));
       else if(parent_of(superconcept1, subconcept1) ^ 
parent_of(superconcept2, subconcept2))
          subconcept1 ←superconcept2;
          contentconstraint = true;
          write(parent_of(superconcept1,subconcept2) ^
datatype_property(superconcept1,rel(int)) ➔
target_class(subconcept2));
  }}
```