Introduction

The dataset provides a comprehensive look at the top 240 restaurants recommended in New York City according to Yelp. It aims to offer insights into consumer preferences, restaurant characteristics, and the overall dining landscape in one of the world's culinary capitals. The data was collected using a Python script that leverages web scraping techniques to extract relevant information from Yelp's website. 240 restaurants are identified and their URLs are retrieved.

Only the latest 10 comments/reviews are collected for each restaurant. The objective is to unveil the recent performance of the restaurants rather than that in their lifetime.

Methodology

The Python script uses the requests library to fetch the HTML content of Yelp's search results for top-recommended restaurants in New York. It employs BeautifulSoup from the bs4 library to parse the HTML and extract the required data.

Summary of Results

The dataset contains the following columns:

CommentDate: The date when the comment/review was posted.
Date: The date when the data was scraped.
RestaurantName: The name of the restaurant.
Comment: The text of the user's comment/review.
Address: The physical address of the restaurant.

Suggested Usages of the Dataset

Sentiment Analysis:
Analyze customer reviews to gauge public sentiment about different restaurants.

Trend Analysis:
Identify popular cuisines or dining styles based on the frequency of certain keywords.

Location-Based Recommendations:
Use the address data to recommend restaurants in specific neighborhoods.

Time Series Analysis:
Examine how restaurant popularity changes over time by comparing the CommentDate and Date columns.

Competitive Analysis:
Identify direct competitors and understand their strengths and weaknesses through consumer reviews.

By leveraging this dataset, stakeholders can gain valuable insights into the New York restaurant scene, which can be instrumental for business strategy, academic research, or personal use.