Uber Review Analysis

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.title[
# Uber Review Analysis
]
.subtitle[
## Developing Data Products JHU Coursera Course Project
]
.author[
### Anthony DiFiore
]
.date[
### 2025/03/12
]

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#About the Data:

*   **Source:** “Uber Customer Reviews Dataset (2024)” from Kaggle
*   **Content:** 1,200 reviews from the Google Play Store
*   **Details:** Ratings (1–5), user feedback, and review times
*   **Preparation:** Data is cleaned and anonymized
*   **Focus:** Explore sentiment patterns, common word pairs (bigrams), and word frequency trends

<img src="dataset-cover.jpg" width="1057" />
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#How to Use the Dashboard:

###**Page 1** – Treemap & Reviews:
*   **Select Sentiment:** Choose sentiment (All/Positive/Negative) and then click on the interactive treemap to zoom in on the most common bigrams per Uber score
*   **Uber Score Reviews Table:** The reviews table below the treemap displays full review texts based on your selection

###**Page 2** – Wordcloud:
*   **Toggle Sentiment:** Use radio buttons to select “Positive” or “Negative” reviews
*   **Frequency Slider:** Set the minimum word frequency to update the wordcloud in real-time

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#[Click here](https://ardifiore.shinyapps.io/uber-review-analysis/) to visit the dashboard!

####Afterwards, [click here](https://github.com/ardifiore/uber-analysis) to visit our GitHub repository
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## References & Citations

*   Data Source: Uber Customer Reviews Dataset (2024) – Kaggle DOI: [https://doi.org/10.34740/KAGGLE/DSV/10248932](https://doi.org/10.34740/KAGGLE/DSV/10248932)
*   Lexicons: VADER, Bing, NRC Emotion, and AFINN
*   Key R Packages: bs4Dash, shinydashboard, tidyverse, d3treeR, DT, wordcloud2, viridis, tidytext
*   Further Reading: Click the provided links for more details on each resource