Do influential Yelp users give higher ratings to take-out restaurants compared to non-influential users, and how does the factor of location affect these ratings?
Since its launch in 2004, Yelp, as one of the largest online review platforms, has played a crucial role in shaping the reputations of businesses, particularly in the food industry. While there is an abundance of research analyzing Yelp reviews (Agarwal, Pelullo, & Merchant 2019; Arthur, Etzioni, & Schwartz, 2019), most studies focus on general patterns of consumer behavior (Fogel, J. and Zachariah, S., 2017), sentiment analysis (Guerreiro & Rita, 2020), or the detection of fake reviews (Lee, Ham, Yang, & Koo, 2018). What these studies largely overlook, however, is the significant role that influential users—those with elite status, numerous followers, or high review counts—play in shaping business ratings (Pranata, I. and Susilo, W., 2016). These users wield disproportionate influence on the platform, and their ratings are often perceived as more credible and trustworthy than those of regular users (Tucker, T., 2011).
In competitive sectors like the food industry, where reputation can make or break a business, understanding the impact of these influential users is essential, as evidenced in a study conducted by Nakayama and Wan (2018) about one-third of customers rely on online reviews when choosing a restaurant and over half of 18-to-34-year-olds factor reviews into their dining decisions. Moreover, while the food industry on Yelp has been extensively studied (Anderson and Magruder, 2012) because of its tremendous impacts on business outcomes (Luca, 2016), the take-out restaurant niche has received minimal attention. The majority of existing studies on take-out restaurants emphasize negative aspects such as health risks, obesity, and food safety concerns (Jeffery et al., 2006; Baek et al., 2022). These studies tend to focus on the health implications of frequent take-out consumption but fail to address the consumer dynamics on platforms like Yelp, where reviews have the power to influence public perception and business success (Jiménez, et al. 2013). Additionally, location, particularly in terms of region, state, and city, plays a pivotal role in how take-out restaurants are rated on Yelp (Tayeen, et al., 2019). Regional preferences can influence the types of cuisines that receive higher ratings, with some regions showing a stronger preference for local or niche food options (Rahimi, et al., 2018). State-level factors such as regulations, food safety laws, and even cultural food trends may also impact how users perceive and rate take-out establishments. Additionally, within cities, neighborhood dynamics like the level of urbanization, socio-economic status, and competition with nearby restaurants can lead to significant variations in ratings (Tayeen, et al., 2021). Influential Yelp users in major metropolitan areas may also drive more reviews and higher visibility for restaurants, skewing ratings compared to those in smaller cities or rural areas. This gap in the literature calls for a deeper investigation into how influential users interact with take-out restaurants, especially as take-out has become increasingly important in the post-pandemic dining landscape.
The accessibility and usefulness of the output from this study significantly benefit other students and the larger scientific community. By developing an automated and reproducible workflow using open-source tools like R, this research provides a template that others can easily adapt for similar analyses. The study's findings and the associated code can be shared on public platforms like GitHub, making them readily available for educational purposes and further research. The workflow includes data extraction, cleaning, transformation, and modeling processes, all documented and scripted to ensure transparency and repeatability. Moreover, the comprehensive HTML report serves as a valuable resource that clearly communicates the research methods, analyses, and findings. It can be used as a teaching tool in academic settings, demonstrating how to approach complex data analyses and interpret results within a real-world context.
This study aims to address the gap in the dynamics of online reviews, particularly from influential Yelp users, by asking: Do influential Yelp users give higher ratings to take-out restaurants compared to non-influential users, and how does the factor of location affect these ratings? This research is critical for several reasons. First, understanding the behavior of influential users could help businesses better manage their online reputations, particularly in a niche market like take-out dining. Second, examining how location—whether at the regional, state, or city level—impacts ratings can provide valuable insights into consumer preferences. Location-specific factors, such as regional food trends, city demographics, and neighborhood characteristics, may influence how users perceive and rate businesses. For take-out restaurants operating in highly competitive environments, especially those without an established offline presence, this research could offer practical strategies for improving ratings and attracting new customers by understanding how geographic context shapes consumer perceptions.
Variable | Description | Data Class | Dataset |
---|---|---|---|
business_id | A unique character string for each individual business | numeric | business dataset |
name | The business name | character | business dataset |
city | The city name where the business is located | character | business dataset |
state | The state name where the business is located | character | business dataset |
stars | The average star rating rounded to half-stars | numeric | business dataset |
review_count | The number of reviews | numeric | business dataset |
is_open | Variable that shows with 0 or 1 of the business is closed or open | numeric | business dataset |
attributes | Business attributes to values | character | business dataset |
user_id | An unique character string as user id | numeric | user dataset |
review_count | The number of reviews | numeric | user dataset |
fans | The number of fans the user has | numeric | user dataset |
elite | The years the user was 'elite' | numeric | user dataset |
user_id | An unique character string as user id | numeric | review dataset |
business_id | A unique character string for each individual business | numeric | review dataset |
review_id | Unique review id | numeric | review dataset |
stars | The average star rating rounded to half-stars | numeric | review dataset |
Independent Variable: Type of Yelp Review
- This variable represents the categorization of Yelp reviews into two groups: Elite and Non-Elite reviews. The main effect of this variable is to examine how being an elite reviewer or a non-elite reviewer influences the star rating of take-out restaurants. Specifically, we aim to determine whether elite reviewers provide systematically different ratings compared to non-elite reviewers.
Quasi-Moderator (State Region): Midwest, Northeast, South, West
- The state region acts as a quasi-moderator in the model. It includes four categories: Midwest, Northeast, South, and West. The main effect of this quasi-moderator is on the star rating of take-out restaurants, allowing us to determine how different regions influence the overall ratings.
- Additionally, interaction effects between the independent variable (type of Yelp review) and the quasi-moderator (state region) will be analyzed. This means we are interested in whether the influence of being an elite or non-elite reviewer on the star ratings of take-out restaurants changes based on the region. For example, elite reviewers in the Midwest might rate take-out restaurants differently compared to elite reviewers in the West.
- Elite Review: This variable indicates whether the review was made by an elite Yelp user or a non-elite user. The main effect of this variable is to assess whether there are systematic differences in the star rating of take-out restaurants based on the reviewer status. Specifically, we aim to determine if elite reviewers provide higher or lower ratings compared to non-elite reviewers.
- User Review Count: This variable represents the total number of reviews a user has submitted. The main effect of this variable is to explore how user engagement (as measured by the number of reviews) impacts the star rating of take-out restaurants. We aim to understand if prolific reviewers tend to give higher or lower ratings compared to those with fewer reviews.
- Fans: This variable captures the number of fans a user has, serving as an indicator of their influence within the Yelp community. The main effect of this variable is to determine how a user's popularity impacts their ratings of take-out restaurants, allowing us to see if more influential users provide systematically different ratings.
- Is_Open: This variable indicates whether the restaurant is currently open or closed at the time of rating. The main effect of this variable is to investigate whether the operational status of a restaurant influences the star rating of take-out restaurants. Specifically, we are interested in whether open restaurants are rated differently compared to those that are closed.
- Star Rating of Take-Out Restaurants: The outcome we are interested in predicting is the star rating given to take-out restaurants. By analyzing the effects of elite status, user review count, user fans, and restaurant operational status, we aim to gain a deeper understanding of the factors influencing these ratings.
Dependent Variable
- Star Rating of Take-Out Restaurants: The outcome we are interested in predicting is the star rating given to take-out restaurants. By analyzing the effects of elite status, user review count, user fans, and restaurant operational status, we aim to gain a deeper understanding of the factors influencing these ratings.
From the Yelp database the business, user, and review data files are downloaded to answer the research question.
- Yelp Database: https://www.yelp.com/dataset/download
Since the file is in .tar format, we need to extract it into separate JSON files by using R. The next step is to convert the JSON files into CSV format using Python code. You can find the R & Python code here:
Finally, run the script from your terminal with the following command and replace yelp_academic_dataset.json with the path to the JSON file you wish to convert. The script will generate a CSV file with the same name as your input JSON file, located in the same directory.
python json_to_csv_converter.py yelp_academic_dataset.json
To streamline the process, we've provided an R script that automatically downloads all the necessary data sets for this study only. Simply copy and run the code, and it will retrieve all required files directly into your working directory. However, please be aware that for the code to function properly, you’ll need to install both the googledrive and tidyverse packages. These packages are crucial, especially when dealing with large files, as they ensure smooth data handling and integration within your R environment.
- Link R script: LINK
Note: Due to limitations such as computational memory constraints and the focus on a manageable dataset for in-depth analysis, we have decided to analyze a reduced sample of 323,856 observations from the Yelp dataset. This sample size allows us to perform comprehensive statistical analyses while maintaining computational efficiency. Future research can extend this work by examining the entire population of Yelp reviews, which could validate our findings on a larger scale and potentially reveal additional insights into the behavior of influential users in the context of take-out restaurants.
To explore these relationships, ANOVA will be used as the primary research method. This method is appropriate for comparing the main effects of the independent variable (type of Yelp review) and the quasi-moderator (state region) on the dependent variable (star rating of take-out restaurants). Specifically, ANOVA will allow us to determine whether there are statistically significant differences in star ratings based on the type of reviewer (elite vs. non-elite) and how these effects interact with the state region. The analysis will incorporate columns such as review_id, user_id, business_id, stars_user, review_count_user, fans, state, city, stars_business, review_count_business, is_open, elite_review, division, region, and take_out. By examining these factors, ANOVA will help reveal any meaningful differences in ratings attributable to both reviewer influence and regional variation.
To complement the initial ANOVA analysis, regression analysis will be used to provide a deeper understanding of the relationships between the independent variables and the star rating of take-out restaurants. While ANOVA identifies significant differences in star ratings based on reviewer type and region, regression allows us to quantify the unique contributions of multiple predictors, such as user review count, fans, is_open, and restaurant location. This approach helps determine the effect size and direction of these influences, providing a comprehensive view of how reviewer characteristics and location impact ratings.
In this stage, we use box-plots and histograms to explore each variable, uncover patterns, and highlight outlines within the data set. Effective data visualization enables us to quickly identify trends, distributions, and issues that might not be apparent from numerical summaries alone.
Based on the histograms and boxplots, it is evident that the variables review_count_user, fans, and review_count_business contain a significant number of outliers. These outliers suggest that there are users and businesses with exceptionally high review counts or fan numbers, deviating far from the typical values in the dataset. Additionally, the distributions of these variables are heavily skewed to the right, indicating that the majority of the data points cluster toward lower values, with a long tail extending towards higher numbers. This right-skewed distribution suggests that while most users and businesses have relatively low review counts and fan numbers, a small subset exhibits much higher counts, likely contributing to the observed outliers.
Distribution of User Ratings by Elite Review Status: To understand how user ratings vary between elite and non-elite reviewers, we analyzed the distribution of stars_user across both groups. As shown in Figure 1, non-elite reviewers give significantly higher ratings than elite reviewers. Specifically, around 30,000 elite reviews have 5-star ratings, whereas over 100,000 non-elite reviews achieve the same. This suggests that non-elite users may have a more positive perception or are more generous in their evaluations.
Average Rating by Elite Status: Focusing on distribution, we assessed whether elite reviewers rate them differently. Figure 2 below shows that elite and non-elite reviewers give an average rating of 4.0 stars, this indicates both parties have the same attitude towards take-out establishments.
Distribution of User Ratings by Region and Elite Status: From our perspective, geographic location can influence consumer preferences and ratings. In order to examine this, we examined the average ratings of user ratings for take-out restaurants across regions by calculating the average user rating (avg_rating) for each region, segmented by elite_review status. Figure 3 illustrates that for all regions, elite users tend to give more generous ratings, especially in the South (3.95 stars). Conversely, non-elite reviewers tend to give similar average ratings (3.7 stars), indicating behavioral differences in rating behaviors between 2 parties.
Impact of Business Open Status and Review Counts: Furthermore, we examined the review_count_business to see how it varies between open and closed businesses. Figure 4 shows that open businesses generally have higher review counts, with a peak of around 7500 reviews, while closed businesses peak at around 200-500 reviews. This could imply that higher engagement correlates with business longevity.
To explore whether the volume of reviews affects user ratings, we analyzed businesses with more than 1,000 reviews and those with fewer than 1,000 reviews to have a general overview.
High Review Counts (>1000): Figure 5 reveals that for businesses with high review counts, elite reviewers consistently give higher average ratings across all regions, except the Midwest. For example, in the West region, elite reviewers give an average rating of 4.2 stars, while non-elite reviewers give 3.98 stars.
Figure 6 indicates that open businesses receive higher average ratings from both elite (4.0 stars) and non-elite (4.1 stars) reviewers compared to closed businesses. However, the interesting thing is that for closed businesses, the average ratings rated by elite users were nearly similar to the surviving businesses, around 4.0 stars. To some extent, it implies the influence of elite users on the survival of businesses is not significant.
Low Review Counts (<1000): Similarly, for businesses that have lower reviews, Figure 7 shows a more consistently significant gap between elite and non-elite reviewers, for which elite users rate those businesses higher compared to their non-elite peers across all regions. These findings imply that elite reviewers tend to rate unpopular businesses more favorably, possibly due to better experiences, higher expectations met, or simply because of sponsorships.
Figure 8 suggests that for businesses with low review counts, the difference in average ratings between open and closed businesses is more dramatical, regardless of elite status.
Relationship Between Number of Fans and User Rating: We explored whether a user's popularity (fans) affects their ratings (stars_user), considering the business's open status and region. Figure 9 presents scatterplots of fans vs. stars_user, colored by is_open and faceted by region. The red regression lines indicate the trend within each region. From these plots, we can see that there is a high correlation between the number of fans and users' ratings. This relationship is dramatically demonstrated in all 4 regions.
A Factorial ANOVA was conducted to compare the main effects of Review Type and State Regions and the interaction between Review Type and State Regions on User Star Ratings of Takeout Restaurants. Review Type included two levels (Elite Review, Non-Elite Review) and State Regions types included four levels (Midwest, Northeast, South, and West). All effects were statistically significant at the .05 significance level.
The main effect for Review Type was significant, F(1, 323848) = 1419,98, p < .001, with a small effect size, η2 = 0.004, 95% CI [0.00, 1.00], indicating a significant difference between the two review types. Specifically, the mean rating for non-elite reviewers (M = 3.74, SD = 1.49) was lower than that for elite reviewers (M = 3.95, SD = 1.05), suggesting that elite reviewers tend to give higher ratings.
The main effect of State Region was significant, F(3, 323848) = 71.79, p < .001, with a smaller effect size, η2 = 0.0007, 95% CI [0.00, 1.00], indicating that there were significant differences in mean ratings across regions. Specifically, the mean ratings were as follows: Midwest (M = 3.81, SD = 1.38), Northeast (M = 3.73, SD = 1.38), South (M = 3.82, SD = 1.39), and West (M = 3.79, SD = 1.43).
Pairwise comparisons with Bonferroni adjustment revealed that the Northeast had significantly lower mean ratings compared to the Midwest (p < .001), South (p < .001), and West (p < .001). The South had significantly higher mean ratings compared to the Northeast (p < .001) and the West (p < .001), but not significantly different from the Midwest (p = .146). The Midwest and West were not significantly different (p = .062).
The interaction effect of Review Type x State Region was also significant, F(3, 323848) - 12.29, p < .001, with a very small effect size, η2 = 0.0001, 95% CI [0.00, 1.00].
These results indicate that both Review Type and State Region had statistically significant, though small, effects on rating, with the Northeast region generally showing the lowest ratings, and elite reviewer giving slightly higher ratings overall. The interaction between the Review Type and State Region had a minimal influence on the results.
A multiple linear regression was calculated to predict User Ratings (stars_user) based on Elite Review Status (elite_review), Review Count (review_count_user), Number of Fans (fans), and whether the Business is Open (is_open). A significant regression equation was found, F(4, 323851) = 603.4, p < .001, with an R² of .007. This indicates that approximately 0.74% of the variance in user ratings is explained by the variables included in the model, suggesting that other factors not included in the model may also contribute significantly to predicting user ratings.
Participants predicted user rating is equal to 3.59 + 0.2315 (elite_review) - 0.0000868 (review_count_user) + 0.0001787 (fans) + 0.1824 (is_open), where:
-
elite_review is coded as 1 if the user has elite status and 0 otherwise,
-
is_open is coded as 1 if the business is open and 0 if it is closed.
User ratings increased by 0.2315 stars for users with elite status and by 0.1824 stars for businesses that are open. Both elite_review and is_open were significant predictors of user ratings (p < .001). While review_count_user had a small negative effect (with user ratings decreasing by 0.0000868 stars for each additional review written), and fans had a small positive effect (with user ratings increasing by 0.0001787 stars for each additional fan), their contributions to the model were also statistically significant.
In conclusion, while elite status and the open status of a business have a noticeable positive impact on user ratings, the overall explanatory power of the model is quite low, as indicated by the R² value. This suggests that other unexamined factors are likely influencing user ratings. Future research could explore additional variables, such as service quality or price, to gain further insights into the factors that influence users’ ratings.
The analysis conducted in this study holds significant relevance for a diverse range of stakeholders, including take-out restaurant owners, online review platforms, consumers, and the academic community. By shedding light on the influence of elite Yelp users and the impact of location on restaurant ratings, the research addresses critical gaps in the current understanding of online consumer behavior within the niche of take-out dining.
- For Business Owners and Marketers: Understanding that influential Yelp users wield disproportionate power over restaurant ratings enables take-out businesses to strategize more effectively. Restaurants can prioritize engaging with these users through exceptional service, personalized experiences, or targeted marketing campaigns. By recognizing the nuances of how location affects ratings, businesses can tailor their offerings to meet regional preferences and cultural tastes. This is especially pertinent in the post-pandemic era, where take-out services have surged, and competition is fierce. Insights from this study could inform menu development, promotional strategies, and customer engagement practices that resonate with local consumers and influential reviewers alike.
- For Online Review Platforms: Platforms like Yelp could utilize the findings to refine their algorithms and user interface designs. By understanding the outsized impact of elite users, platforms might consider weighting systems that balance reviews more equitably or highlight a diversity of opinions. Additionally, recognizing location-based disparities in ratings could lead to the development of more localized review aggregation, helping users find the most relevant and accurate information based on their geographic context.
- For the Academic and Scientific Community: The research contributes to the broader literature on online reviews, influencer impact, and consumer behavior in digital marketplaces. By focusing on a previously underexplored niche—take-out restaurants—and incorporating the variable of location, the study opens new avenues for scholarly inquiry. Future research can build upon the methodology and findings to explore other sectors or delve deeper into regional cultural influences on consumer behavior.
- Future Applications: Given the increasing reliance on online platforms for consumer decision-making, the implications of this study are far-reaching. Businesses can develop strategies to engage with influential users proactively, potentially improving their ratings and attracting new customers. Online platforms might implement features that promote a more balanced representation of user opinions. Academics can use the study as a springboard for further exploration into the dynamics of online influence and regional consumer preferences.
This repository contains a structured workflow for downloading, preparing, preprocessing, and analyzing data to find insights about take-out restaurant ratings. The process is automated using a Makefile, ensuring each step runs in sequence.
├── README.md
├── makefile
├── updated-project
├── .Rhistory
├── .RData
├── .gitignore
├── 1-docs
│ ├── Group Report.Rproj
│ ├── Group Report.HTML
├── 2-temporary_data
│ ├── cleaned_sample_data
│ ├── sample_data_elite_encoder
│ ├── business_data.csv
│ ├── user_data.csv
│ ├── review_data.csv
│ ├── sample_data.csv
├── 3-final_data
│ ├── takeout_data.csv
├── 4-testing_code
│ ├── download_files_automatically.R
│ ├── label_elite_users.R
│ ├── matching_elite_format.R
│ ├── tar_format_to_json_converter.R
├── 5-external_code
│ ├── json_to_csv_converter.py
├── 6-source_code
│ ├── 0_install_packages.R
│ ├── 1_download_data.R
│ ├── 2_prepare_data.R
│ ├── 3_preprocessing_data.R
│ ├── 4_plot_data.R
│ ├── 5_analyse_data.R
├── 7-plots
├── 8-results
Please follow the installation guides at LINK
- Python: Installation guide
- R: Installation guide
- Make: Installation guide
- GitBash: Installation guide
- Quarto: Installation guide
To knit Quarto documents, make sure you have installed the Quarto software and set PATH in the environment variables
For Python, make sure you have installed the following packages:
pip install argparse
pip install csv
pip install json
pip install os
For R, make sure you have installed the following packages:
install.packages("here")
install.packages("rstudioapi")
install.packages(c("tidyverse", "googledrive", "readr", "dplyr"))
install.packages("reticulate")
install.packages("knitr")
install.packages("ggcorrplot")
install.packages("sjPlot")
install.packages("sjmisc")
install.packages("car")
install.packages("cowplot")
install.packages("broom")
install.packages("effectsize")
install.packages("emmeans")
install.packages("gridExtra")
To execute this study, the source code will be run in the correct sequence, ultimately producing the analysis results. You can run the Makefile
by following these steps:
- Clone the Repository:
- Fork this repository and you will have the link
https://github.com/{your username}/yelp-influencers-takeout-ratings
- Open GitBash in your preferred file location
- Type
- Fork this repository and you will have the link
git clone https://github.com/{your username}/yelp-influencers-takeout-ratings
- Set the Working Directory:
- Type
cd yelp-influencers-takeout-ratings
- Run the Workflow:
- Open the Command Prompt (terminal) in the working directory
- Type
make
- Wait approximately 20 - 30 minutes for the entire project to run
- Clean Temporary and Final Data:
- Type
make clean
Note: If you want to run each source code separately, this should be done in the following order:
- 0_install_packages.R
- 1_download_data.R
- 2_prepare_data.R
- 3_preprocessing_data.R
- 4_plot_data.R
- 5_analyse_data.R
Run the source code separately:
- Open the Command Prompt (terminal) in the working directory
- Type
Rscript <file_name.R>
This repository was created for the Data Preparation and Workflow Management course, taught by Hannes Datta at the Tilburg School of Economics and Management, as part of the Master's program in Marketing Analytics. It is a collaboration by Team 12, consisting of:
- Kris Bruurs - [email protected]
- Ly Ba Tho - [email protected]
- Jelle de Bie - [email protected]
- Zeynep Yavlal - [email protected]
- Bart van de Mortel - [email protected]
- Agarwal, A.K., Pelullo, A.P. and Merchant, R.M., 2019. “Told”: the word most correlated to negative online hospital reviews. Journal of General Internal Medicine, 34, pp.1079-1080.
- Anderson, M. and Magruder, J., 2012. Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. The Economic Journal, 122(563), pp.957-989.
- Arthur, J.R., Etzioni, D. and Schwartz, A.J., 2019. Characterizing extremely negative reviews of total joint arthroplasty practices and surgeons on Yelp. Arthroplasty Today, 5(2), pp.216-220.
- Baek, S., Suk, Y., Lee, H. and Ham, S., 2022. The influence of customer perception about food safety on the use of restaurant food delivery or takeout. Journal of the Korean Dietetic Association, 28(3), pp.182-194.
- Fogel, J. and Zachariah, S., 2017. Intentions to use the Yelp review website and purchase behavior after reading reviews. Journal of Theoretical and Applied Electronic Commerce Research, 12(1), pp.53-67.
- Guerreiro, J. and Rita, P., 2020. How to predict explicit recommendations in online reviews using text mining and sentiment analysis. Journal of Hospitality and Tourism Management, 43, pp.269-272.
- Jeffery, R.W., Baxter, J., McGuire, M. and Linde, J., 2006. Are fast food restaurants an environmental risk factor for obesity? International Journal of Behavioral Nutrition and Physical Activity, 3, pp.1-6.
- Jiménez, F.R. and Mendoza, N.A., 2013. Too popular to ignore: The influence of online reviews on purchase intentions of search and experience products. Journal of Interactive Marketing, 27(3), pp.226-235.
- Lee, K., Ham, J., Yang, S.B. and Koo, C., 2018. Can you identify fake or authentic reviews? A fsQCA approach. In Information and Communication Technologies in Tourism 2018: Proceedings of the International
- Conference in Jönköping, Sweden, January 24-26, 2018 (pp. 214-227). Springer International Publishing.
- Luca, M., 2016. Reviews, reputation, and revenue: The case of Yelp.com. Com (March 15, 2016). Harvard Business School NOM Unit Working Paper, (12-016).
- Nakayama, M. and Wan, Y., 2019. The cultural impact on social commerce: A sentiment analysis on Yelp ethnic restaurant reviews. Information & Management, 56(2), pp.271-279.=
- Pranata, I. and Susilo, W., 2016. Are the most popular users always trustworthy? The case of Yelp. Electronic Commerce Research and Applications, 20, pp.30-41.
- Rahimi, S., Mottahedi, S. and Liu, X., 2018. The geography of taste: using Yelp to study urban culture. ISPRS International Journal of Geo-Information, 7(9), p.376.
- Tayeen, A.S.M., Mtibaa, A., Misra, S. and Biswal, M., 2021. Impact of Locational Factors on Business Ratings/Reviews: A Yelp and TripAdvisor Study. Big Data and Social Media Analytics: Trending Applications, pp.25-49.
- Tayeen, A.S.M., Mtibaa, A. and Misra, S., 2019, August. Location, location, location! quantifying the true impact of location on business reviews using a Yelp dataset. In Proceedings of the 2019 IEEE/ACM
- International Conference on Advances in Social Networks Analysis and Mining (pp. 1081-1088).
- Tucker, T., 2011. Online word of mouth: characteristics of Yelp.com reviews. Elon Journal of Undergraduate Research in Communications, 2(1), pp.37-42.