Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning

Zahoor, Kanwal; Bawany, Narmeen Zakaria; Hamid, Soomaiya

doi:10.1109/acit50332.2020.9300098

Cited by 36 publications

(23 citation statements)

References 23 publications

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“…Nowadays, all businesses are offering their products and services online. These sites allow consumers to share their experiences and recommendations about these businesses' products, places, and services on different platforms such as TripAdvisor, Yelp, Facebook and Jeeran [3].…”

Section: Introductionmentioning

confidence: 99%

“…These reviews may affect the customer's final choice since people trust customers' reviews more than advertisements produced by a company. Furthermore, customers' experiences can be enhanced by utilizing other customers' suggestions [3].…”

Section: Introductionmentioning

confidence: 99%

“…Sentiment analysis is essential for every business as it can be used to improve the decisions of customers, business owners, and service providers [17]. SA is used by business owners to enhance their businesses' image and increase their success [3] since it helps decision-makers improve the quality of their products and services based on their customers' reviews; thus, the business can provide more praiseworthy services. This leads to higher customer satisfaction and more sales and revenues for the business [12].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have followed a Machine Learning (ML) approaches for restaurants sentiment analysis. A study done by Zahoor et al [3] used NB Classifier, logistic regression, SVM, and RF methods to analyze customers' sentiments about restaurants in Karachi. The study annotated 4000 reviews from a well-known Pakistani Facebook community called SWOT'S.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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Online media has an increasing presence on the restaurants' activities through social media websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers' reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and AUC, for different versions of the datasets.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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“…The binary classes will be described as "like" and "dislike," with "1" denoting "like" and "0" denoting "dislike". Text encoding, tokenization, translating uppercase to lowercase, lemmatization, and other text-based features were used to create this model [8].…”

Section: Introductionmentioning

confidence: 99%

Food Reviews Classification using multi-label convolutional neural network text classifier

Lasne¹,

Nandrekar²,

Khan³

et al. 2021

ITM Web Conf.

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Most commercial websites, such as Amazon, encourage users to leave reviews of the goods and services they get after purchasing them. For certain consumers, this analysis is critical when determining whether or not to buy a product. Understanding the consequences of feedback and correctly classifying their utility may therefore be an advantageous method for websites. The classification results can also be used as a review and recommendation program for ongoing success. Nowadays people visit several restaurants on various occasions. They get confused most of the time after having a look at the food menu. Based on the ratings and reviews of the dish it becomes easier for them to decide the dish they wish to order. However they unable to read each review of the previous customers. So to overcome this issue, we have proposed NLP (Natural Language Processing) technique and Spacy CNN (Convolutional Neural Network) pipeline system which will classify all the reviews in a single rating. Each review is labelled with a reviewer's score indicating the sentiment of the reviewers. Our task is to predict a reviewer’s score on a scale of 0 or 1. Where 1 indicates the users like the dish while 0 indicates that the reviewers were not satisfied with the dish.

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Sentiment Analysis of Restaurant Reviews Using Machine Learning

Abdullah

Waheed

Hossain

2023

Lecture Notes in Networks and Systems

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Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning

Cited by 36 publications

References 23 publications

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Food Reviews Classification using multi-label convolutional neural network text classifier

Sentiment Analysis of Restaurant Reviews Using Machine Learning

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