An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis.The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives.Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields.Various subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for sentiment analysis.The rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them all.According to a recent study,81%of consumers conduct online research prior to making a purchase.But the reviews available online are too huge and numerous for human brains to process and analyze.Hence,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so on.Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market research.Therefore,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words.The dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this study.The performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.

Data Analytics for the Identification of Fake Reviews Using Supervised Learning

Fake reviews,also known as deceptive opinions,are used to mislead people and have gained more importance recently.This is due to the rapid increase in online marketing transactions,such as selling and purchasing.E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased.New customers usually go through the posted reviews or comments on the website before making a purchase decision.However,the current challenge is how new individuals can distinguish truthful reviews from fake ones,which later deceives customers,inflicts losses,and tarnishes the reputation of companies.The present paper attempts to develop an intelligent system that can detect fake reviews on ecommerce platforms using n-grams of the review text and sentiment scores given by the reviewer.The proposed methodology adopted in this study used a standard fake hotel review dataset for experimenting and data preprocessing methods and a term frequency-Inverse document frequency(TF-IDF)approach for extracting features and their representation.For detection and classification,n-grams of review texts were inputted into the constructed models to be classified as fake or truthful.However,the experiments were carried out using four different supervised machine-learning techniques and were trained and tested on a dataset collected from the Trip Advisor website.The classification results of these experiments showed that na飗e Bayes(NB),support vector machine(SVM),adaptive boosting(AB),and random forest(RF)received 88%,93%,94%,and 95%,respectively,based on testing accuracy and tje F1-score.The obtained results were compared with existing works that used the same dataset,and the proposed methods outperformed the comparable methods in terms of accuracy.

Development of a Comparison Based Hotel and Resort Booking System in Bangladesh

With the rise of population and the industrial revolution, it is obvious that hotels and resorts are increasing drastically day by day. In the 21st century, tourism is a trend. Everyone loves to travel and the reasons vary from person to person. Whether it is to have a cheerful and peaceful mind and place or business purposes, either way, hotels and resorts actually doing their jobs very well. This, tourism is a promising sector for any country and Bangladesh is no different. But the problem is we have to do the hotel booking and reservation manually most of the time. Sometimes it is too troublesome and knowing about the suitable hotels and resorts is too difficult. Though there are some online platforms available, they are not our country-oriented such as the payment system is not preferable for us. To solve this problem Vhromon is developed, a comparison-based hotel and resort booking system where anyone can book a hotel or resort by just login from the internet. Vhromon is an interactive online platform and it is user-friendly and easy to get like most of the platforms available nowadays. But they do not have all the hotels and resorts enlisted, only the well-known ones. On the other hand, Vhromon has everything enlisted where customers can compare them and can choose the right one for them. Furthermore, this system has a vendor panel where any hotel or resort owner can add their hotel or resort easily without any hassle, unlike the existing one.