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TikTok Customer Sentiment Data
Social Media and Posts
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset contains a collection of user reviews for the TikTok application available on the Google Play Store. TikTok, known as Douyin in China, is a highly popular video-focused social networking service owned by ByteDance Ltd., hosting a variety of short-form user-generated videos. Launched internationally in 2017 and expanded globally in 2018 after merging with Musical.ly, it has quickly amassed over 2 billion mobile downloads worldwide by October 2020. This particular dataset offers insights into user feedback and experiences through comments and ratings, serving as a valuable resource for understanding public sentiment regarding the app.
Columns
- userName: The display name of the user who submitted the review.
- userImage: The URL for the profile image associated with the user.
- content: The textual comment or feedback provided by the user.
- score: The rating given by the user, ranging from 1 to 5 stars.
- thumbsUpCount: The total number of 'thumbs up' or likes a specific user comment received.
- reviewCreatedVersion: The version number of the TikTok app on which the review was created.
- at: The date and time when the user review was created.
- replyContent: The response, if any, made by the company to the user's comment.
- repliedAt: The date and time when the company's reply was issued.
- reviewId: A unique identifier assigned to each individual user review.
Distribution
The dataset is provided in a CSV format, specifically named
tiktok_google_play_reviews.csv, and has a file size of 97.55 MB. It comprises approximately 460,000 records across 10 distinct columns. For instance, the reviewId column contains 460,287 unique values, while userName has 406,759 unique entries. User content includes 278,000 unique comments, with "Good" and "Nice" being common sentiments. User score ratings range from 1 to 5, with a mean score of 4.23 and a high frequency of 5-star ratings (338,894 records). The thumbsUpCount for comments has a mean of 3.21. Notably, reviewCreatedVersion has about 27% missing values, and company replyContent and repliedAt are largely missing, indicating very few official responses to user comments (only 260 valid entries each).Usage
This dataset is ideal for:
- Sentiment analysis: Analysing user comments to gauge overall sentiment towards the TikTok app.
- Market research: Understanding user satisfaction, identifying strengths and weaknesses of the application.
- Product development: Informing app developers about common issues, desired features, and user experience pain points.
- Competitor analysis: Benchmarking user feedback against other social media applications.
- Academic research: Studying mobile app adoption, user engagement, and social media trends.
Coverage
The dataset covers user reviews specifically from the Google Play Store, implying a global scope given TikTok's international reach. The time range for the reviews spans from 18 June 2022 to 30 November 2022. The data reflects feedback from general users of the TikTok app on the Android platform; no specific demographic breakdowns are provided within the sources.
License
CC BY-SA 4.0
Who Can Use It
- Data scientists and analysts: For conducting in-depth sentiment analysis and statistical modelling of user feedback.
- Mobile app developers and product managers: To gather direct user insights for improving app features and addressing reported issues.
- Marketing and brand strategists: To monitor brand perception, track public relations, and inform marketing campaigns.
- Social media researchers: To explore user behaviour, content preferences, and the impact of short-form video platforms.
- Business intelligence professionals: For informing strategic decisions based on customer feedback and market trends.
Dataset Name Suggestions
- TikTok Google Play User Reviews
- TikTok Android App Feedback 2022
- Google Play TikTok App Reviews
- TikTok Customer Sentiment Data
- Mobile TikTok User Reviews
Attributes
Original Data Source: TikTok Customer Sentiment Data
QUALITY