Restaurant setup business analysis using yelp dataset

Hegde, Sindhu B; Satyappanavar, Supriya; Setty, Shankar

doi:10.1109/icacci.2017.8126196

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…The SA task was accomplished by using various datasets, such as the Yelp dataset, to examine the approaches proposed by other researchers as explained by Hegde et al [28] and made public for research and academic studies. The Zomato Restaurant Dataset is derived from the online multinational restaurant aggregator in which reviews are posted alongside information, menus, and delivery options.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

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

et al. 2022

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“…Then, the encoded two spaces are regarded as two potential factors in the matrix factorization process to predict the unknown preference ratings. They conducted experiments on real-world datasets Epinions [34], Yelp [35] and Flixster [36] respectively, and the experimental results indicated that the perform of GNN-SoR is superior to four baselines algorithm such as: SocialMF (Matrix Factorization based Social Recommendation Networks) [37], TrustSVD [38], TrustMF [39], and AutoRec [40].…”

Section: Recommendation Systemmentioning

confidence: 99%

Adversarial Machine Learning on Social Network: A Survey

Guo

2021

Front. Phys.

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In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

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“…In this section, we briefly overview different streams of studies related to predicting business reviews and ratings based on business services [4], features [3], [5]- [7], and the location of business [1], [2]. Researchers have used features e.g., degree centrality and clustering coefficient, derived from graph model of user rating and business category data in Yelp, to predict user ratings [7].…”

Section: Related Workmentioning

confidence: 99%

“…They also demonstrated the influence of local and foreign customers on the popularity of businesses and proposed a model to predict popularity of businesses with an accuracy of 89%. Hegde et al [4] identified restaurant properties such as "accept credit card", "ambience" to be the most interesting to customers, found Monday as the most crowded day of the week for restaurants, and explored other restaurant properties (e.g., Wi-Fi, parking lot) that are missing in nearest restaurants to help setup a new restaurant business.…”

Section: Related Workmentioning

confidence: 99%

Location, location, location!

Tayeen

Mtibaa

Misra

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Today, with the emergence of various business review sites such as Yelp, Trip Advisor, and Zomato, people can write reviews and provide an assessment (often as 1-5 score rating). The success of a business on the crowd-sourced review platform has taken the form of positive reviews and high star ratings (failure are associated with negative reviews and low star ratings). We often claim that location plays a major role in determining the success or the failure of a given business. This paper attempts to verify this claim and quantifies the impact of location, solely, on business success, using two data sets; a Yelp dataset for business information and reviews, and another Location dataset that gathers location-based information in a city or an area. We perform an empirical study to quantify the impact of (i) relative location to well known landmarks and (ii) parameterized location (such as cost of living in a given zip code), on the success of restaurants. In our study, we found that parameterized location using location characteristic parameters such as housing affordability correlate highly with restaurant success with more than 0.81 correlation ratio. We also observe that the closer the restaurant to a landmark (relative location) the more likelihood it succeeds.

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Restaurant setup business analysis using yelp dataset

Cited by 12 publications

References 3 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

Adversarial Machine Learning on Social Network: A Survey

Location, location, location!

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