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Google Play Multilingual Messenger Reviews
Product Reviews & Feedback
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This collection features 6,000 recent customer feedback entries sourced from the Google Play Store, focused on six major messenger applications. The dataset provides valuable insight into user experience and satisfaction levels across different global markets. It incorporates reviews written in five distinct languages, making it highly valuable for cross-cultural analysis and natural language processing applications that require diversity in language data for training and evaluation.
Columns
The data is organised into 13 fields, capturing details about the review and the associated application metrics:
- reviewId: A unique identifier assigned to the specific customer review.
- userName: The name provided by the user who submitted the feedback.
- userImage: The profile image URL associated with the reviewer.
- content: The primary text written by the user in their review.
- score: The star rating (1 to 5) given by the user to the messenger application.
- thumbsUpCount: The total number of likes or up-votes received by the review.
- at: The specific date when the review was created.
- replyContent: The text of the comment, if any, posted by the developer in response to the review.
- repliedAt: The date when the developer's response was posted.
- appVersion: The version of the application the user had installed when writing the review.
- userLang: The two-letter language code of the review (e.g., EN, FR, JP).
- app_id: The unique identifier for the specific messenger application being reviewed.
Distribution
The dataset is structured as a single file, typically provided in CSV format. It contains 6,000 records and 13 columns. The vast majority of the data is valid and non-missing; however, fields related to developer replies (e.g.,
replyContent and repliedAt) and some application version details have a high percentage of missing values.Usage
This data is ideal for a variety of tasks including developing and optimising machine learning models for automatic sentiment classification. It is also suitable for predicting customer satisfaction metrics or extracting critical user feedback points. Its multilingual characteristics particularly benefit applications requiring diverse language inputs, such as large language models (LLMs) and deep learning systems focused on natural language processing (NLP).
Coverage
The dataset focuses exclusively on reviews pertaining to six specific messaging applications: Telegram, Facebook Messenger, WhatsApp, Viber, Snapchat, and WeChat. The temporal scope of the reviews spans from early February 2022 through to September 2023. The linguistic scope includes reviews in English, French, German, Italian, and Japanese.
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Who Can Use It
- Data Scientists: For training models on text classification and sentiment analysis using diverse language data.
- Market Researchers: To analyse public perception, track changes in user satisfaction, and compare performance between competing messenger apps.
- App Developers/Product Teams: To extract actionable insights from user feedback and assess the quality and attentiveness of developer support replies.
Dataset Name Suggestions
- Google Play Multilingual Messenger Reviews
- Global Messaging App Customer Sentiment Data
- Cross-Lingual App Feedback 6000
- Google Play Store Review Dataset
Attributes
Original Data Source: Google Play Multilingual Messenger Reviews
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