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LinkedIn Reviews Sentiment Dataset
Reviews & Ratings
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset contains user reviews and ratings for the LinkedIn mobile application, extracted from its Google Store page. It provides valuable insights into the public's perception of the app over an extended period. The collection of reviews offers a basis for understanding user sentiment, identifying trends, and pinpointing common pain points experienced by users of the LinkedIn app. The dataset is particularly useful for product development teams, market analysts, and researchers interested in user feedback and app performance analysis.
Columns
- index: A numerical index for each review record.
- review_id: A unique identifier for each review.
- pseudo_author_id: A pseudonymised identifier for the author of the review.
- author_name: The name of the author who submitted the review.
- review_text: The textual content of the user's review.
- review_rating: The star rating given by the user, ranging from 1 to 5. Note that some very old reviews may have a zero score.
- review_likes: The number of likes or upvotes a particular review received.
- author_app_version: The version of the LinkedIn app installed when the review was made.
- review_timestamp: The date and time (in UTC) when the review was submitted.
Distribution
This dataset is typically provided as a data file, commonly in CSV format. It comprises approximately 320,000 individual review records. The
review_id column alone contains 322,641 unique values. The data structure is tabular, with each row representing a single review and columns providing specific details about that review. Specific numbers for rows/records are available and consistent with the total count.Usage
This dataset is ideal for a variety of analytical applications and use cases, including:
- Sentiment Analysis: Extracting sentiments and trends from user feedback to gauge overall satisfaction and identify shifts in public opinion.
- Version Performance Tracking: Identifying which versions of the LinkedIn app received the most positive or negative feedback.
- Topic Modelling: Utilising natural language processing (NLP) techniques like topic modelling to uncover specific pain points, frequently requested features, or common praise for the application.
- Product Improvement: Informing product development and user experience (UX) design by directly addressing user feedback.
- Market Research: Understanding user perceptions of a leading professional networking platform.
Coverage
The dataset covers reviews for the LinkedIn app, which has a global user base with over 970 million registered members from more than 200 countries and territories. The reviews themselves were extracted from its Google Store page. The time range for the reviews spans from 7th April 2011 to 18th November 2023. There are specific notes on data availability for certain groups/years visible in the timestamp distribution.
License
CC0
Who Can Use It
This dataset is intended for:
- Data Scientists & Analysts: For performing sentiment analysis, natural language processing, and trend analysis on app reviews.
- App Developers & Product Managers: To gain direct user feedback for product iteration, bug identification, and feature prioritisation.
- Market Researchers: To understand user behaviour, competitive landscape, and public perception within the social media and professional networking domain.
- Academic Researchers: For studies on user feedback, app development cycles, and the evolution of digital platform perception.
Dataset Name Suggestions
- LinkedIn App User Reviews
- Google Play LinkedIn Feedback
- LinkedIn Application Ratings
- LinkedIn Reviews Sentiment Dataset
- Professional Networking App Reviews
Attributes
Original Data Source: 📝 320K LinkedIn App Google Store Reviews
QUALITY