Amazon Customer Reviews Dataset Description This dataset contains customer reviews for products on Amazon, sourced from Trustpilot. It includes essential details about the review experience, such as ratings, review titles, review texts, and additional metadata. This dataset is useful for analyzing customer satisfaction, sentiment, and trends over time. Data Source Website: Trustpilot Collection Method: Web scraping using Python's requests and BeautifulSoup libraries. Dataset Structure The dataset includes the following columns: Reviewer Name: The name or alias of the reviewer. Profile Link: A link to the reviewer's profile. Country: The country of the reviewer. Review Count: The number of reviews submitted by the reviewer. Review Date: The date when the review was posted. Rating: The rating given by the reviewer (e.g., stars). Review Title: A brief title summarizing the review. Review Text: The full text of the review. Date of Experience: The date when the customer experienced the product or service. Potential Uses Sentiment Analysis: Gauge customer sentiment towards products. Customer Satisfaction Tracking: Monitor trends in ratings over time. Product Improvement: Identify common themes for product enhancement. Market Segmentation: Understand regional preferences through demographic data. Competitor Analysis: Compare customer feedback with competitors. Recommendation Systems: Inform algorithms to enhance personalized shopping experiences. Trend Analysis: Correlate sentiment changes with marketing efforts. Collection Methodology Data Source: Trustpilot's Amazon review page. Tools Used: Python libraries (requests and BeautifulSoup) for web scraping. Data Extraction: Key details extracted from HTML elements. Handling Missing Data: Placeholder text used for missing fields. Aggregation: Compiled into a structured dataset. Delays: Managed server load with pauses between requests.

Amazon Customer Reviews Dataset
Description
This dataset contains customer reviews for products on Amazon, sourced from Trustpilot. It includes essential details about the review experience, such as ratings, review titles, review texts, and additional metadata. This dataset is useful for analyzing customer satisfaction, sentiment, and trends over time.

Data Source
Website: Trustpilot
Collection Method: Web scraping using Python's requests and BeautifulSoup libraries.
Dataset Structure
The dataset includes the following columns:

Reviewer Name: The name or alias of the reviewer.
Profile Link: A link to the reviewer's profile.
Country: The country of the reviewer.
Review Count: The number of reviews submitted by the reviewer.
Review Date: The date when the review was posted.
Rating: The rating given by the reviewer (e.g., stars).
Review Title: A brief title summarizing the review.
Review Text: The full text of the review.
Date of Experience: The date when the customer experienced the product or service.
Potential Uses
Sentiment Analysis: Gauge customer sentiment towards products.
Customer Satisfaction Tracking: Monitor trends in ratings over time.
Product Improvement: Identify common themes for product enhancement.
Market Segmentation: Understand regional preferences through demographic data.
Competitor Analysis: Compare customer feedback with competitors.
Recommendation Systems: Inform algorithms to enhance personalized shopping experiences.
Trend Analysis: Correlate sentiment changes with marketing efforts.
Collection Methodology
Data Source: Trustpilot's Amazon review page.
Tools Used: Python libraries (requests and BeautifulSoup) for web scraping.
Data Extraction: Key details extracted from HTML elements.
Handling Missing Data: Placeholder text used for missing fields.
Aggregation: Compiled into a structured dataset.
Delays: Managed server load with pauses between requests.

README.md

@@ -0,0 +1,16 @@
+---
+license: mit
+task_categories:
+- text2text-generation
+- text-generation
+- translation
+- summarization
+- question-answering
+- sentence-similarity
+language:
+- en
+tags:
+- finance
+size_categories:
+- 10K<n<100K
+---