Course Content

ABSTRACT

With more consumers using online opinion reviews to inform their service decision making, opinion reviews have an economical impact on the bottom line of businesses. Online reviews have great impact on today’s business and commerce. Decision making for purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product reviews for their own interest. Unsurprisingly, opportunistic individuals or groups have attempted to abuse or manipulate online opinion reviews (e.g. spam reviews) to make profits, etc, and that detecting deceptive and fake opinion reviews is a topic of ongoing research interest. Users evaluate hotels by using online reviews depend on their various attributes. Hotel booking service providers in the form of websites or online-based applications have provided features where consumers can provide a review regarding their assessment of the hotel. But the number of reviews available makes users unable to filter out all the reviews. Sentiment analysis can be used as a solution to overcome this by classifying reviews into positive or negative sentiments.

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression, Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier, KNN and SVC to predict the Review is Positive Fake or Negative Fake or Positive Real or Negative Real based on review data entered by the user in the front end.

INTRODUCTION

Technologies are changing rapidly. Old technologies are continuously being replaced by new and sophisticated ones. These new technologies are enabling people to have their work done efficiently. Such an evolution of technology is online marketplace. We can shop and make reservation using online websites. Almost, everyone of us checks out reviews before purchasing some products or services. Hence, online reviews have become a great source of reputation for the companies. Also, they have large impact on advertisement and promotion of products and services. With the spread of online marketplace, fake online reviews are becoming great matter of concern. People can make false reviews for promotion of their own products that harms the actual users. Also, competitive companies can try to damage each others reputation by providing fake negative reviews. Researchers have been studying about many approaches for detection of these fake online reviews. Some approaches are review content based and some are based on behavior of the user who is posting reviews. Content based study focuses on what is written on the review that is the text of the review where user behavior based method focuses on country, ip-address, number of posts of the reviewer etc. Most of the proposed approaches are supervised classification models. Few researchers, also have worked with semi-supervised models. Semi-supervised methods are being introduced for lack of reliable labeling of the reviews.

The development of information through the website is currently growing rapidly along with the increasing needs of the community, especially in the hospitality sector. At this time, hotel bookings can also be through websites or smartphones, making it easier for hotel marketing to attract consumers. However, with an online ordering system consumers do not see the hotel directly to be ordered, so it takes consideration to book a hotel that suits the needs of consumers. Growing consumer dependence on online product reviews and services has caused so much usergenerated content that does not have the potential for customers to filter all of these reviews.

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression, Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier, KNN and SVC  to predict the Review is Positive Fake or Negative Fake or Positive Real or Negative Real based on review data entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

 

 

Objective

The main aim of this project is to predict whether a review is fake or real as well as  positive or negative using machine learning techniques, NLP techniques and machine learning algorithms like Logistic Regression, Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier, KNN and SVC with good accuracy based on review data entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

 

 

Problem Statement

Decision making for purchase of online products mostly depends on reviews given by the users. Hence, opportunistic individuals or groups try to manipulate product reviews for their own interests. With more consumers using online opinion reviews to inform their service decision making, opinion reviews have an economical impact on the bottom line of businesses. Unsurprisingly, opportunistic individuals or groups have attempted to abuse or manipulate online opinion reviews (e.g. spam reviews) to make profits, etc, and that detecting deceptive and fake opinion reviews is a topic of ongoing research interest.

 

 

 

 

 

 

 

 

 

 

 

 

Proposed System:

 

We proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression, Naïve Bayes and XGB Classifier, Random Forest classifier, Decision Tree Classifier, KNN and SVC  to predict the Review is Positive Fake or Negative Fake or Positive Real or Negative Real based on review data entered by the user in the front end.