This dataset provides a collection of user reviews for the Threads mobile application from both the Google Play Store and the Apple App Store. It is designed to offer insights into user satisfaction, app performance, and to help identify emerging user patterns and sentiments. The data was gathered by scraping reviews from the respective app marketplaces.

Columns

• source: Indicates the origin of the review, such as 'Google Play' or 'App Store'.
• review_description: Contains the actual text of the review provided by the user.
• rating: Represents the numerical rating given by the user.
• review_date: Specifies the date when the review was submitted.

Distribution

The dataset is typically provided in a CSV file format. Specific row or record counts are not available for the entire dataset, but review counts are detailed for various rating ranges and daily periods. For instance, 15,559 reviews are rated between 4.80 and 5.00, while 11,338 reviews were recorded between 5th and 6th July 2023.

Usage

This dataset is ideal for: * Sentiment analysis to understand overall user sentiment towards the Threads app. * Investigating factors that lead to 1-star and 5-star ratings, offering insights into user satisfaction and dissatisfaction. * Evaluating the application's performance and identifying recurring themes in user feedback.

Coverage

The dataset's geographic scope is global, collecting reviews from users worldwide. The time range for the reviews spans from 6th July 2023 to 25th July 2023. The dataset was last updated on 26th July 2023. It captures feedback from users across two major mobile platforms, Google Play (92% of reviews) and Apple App Store (8% of reviews).

License

CC-BY-NC

Who Can Use It

• Data analysts and researchers interested in mobile app performance and user sentiment.
• App developers and product managers aiming to understand user feedback for app improvements.
• Organisations conducting market research on social media applications.

Dataset Name Suggestions

• Threads Mobile App Reviews 2023
• Instagram Threads User Feedback Dataset
• Threads App Store & Google Play Reviews
• Threads User Ratings and Sentiment Data

Attributes

Original Data Source: Threads, an Instagram app Reviews

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

Context

From the beginning of 2020 to April 8th (the day Wuhan reopened), this dataset summarizes the social media hotspots and what people focused in the mainland of China, as well as the epidemic development trend during this period. The dataset containing four .csv files covers most social media platforms in the mainland: Sina Weibo, TikTok, Toutiao and Douban.

Sina Weibo

a platform based on fostering user relationships to share, disseminate and receive information. Through either the website or the mobile app, users can upload pictures and videos publicly for instant sharing, with other users being able to comment with text, pictures and videos, or use a multimedia instant messaging service. The company initially invited a large number of celebrities to join the platform at the beginning, and has since invited many media personalities, government departments, businesses and non-governmental organizations to open accounts as well for the purpose of publishing and communicating information. To avoid the impersonation of celebrities, Sina Weibo uses verification symbols; celebrity accounts have an orange letter "V" and organizations' accounts have a blue letter "V". Sina Weibo has more than 500 million registered users;[12] out of these, 313 million are monthly active users, 85% use the Weibo mobile app, 70% are college-aged, 50.10% are male and 49.90% are female. There are over 100 million messages posted by users each day. With 90 million followers, actress Xie Na holds the record for the most followers on the platform. Despite fierce competition among Chinese social media platforms, Sina Weibo has proven to be the most popular; part of this success may be attributable to the wider use of mobile technologies in China.[https://en.wikipedia.org/wiki/Sina_Weibo]

Douyin

Douyin (English: TikTok), referred to as TikTok, is a short-video social application on mobile phones. Users can record 15-second short videos, which can easily complete mouth-to-mouth (to mouth), and built-in special effects The user can leave a message to the video. Since September 2016, Toutiao has been launched online and is positioned as a short music video community suitable for Chinese young people. The application is vertical music UGC short videos, and the number of users has grown rapidly since 2017. In June 2018, Douyin reached 500 million monthly active users worldwide and 150 million daily active users in China. [https://zh.wikipedia.org/wiki/%E6%8A%96%E9%9F%B3]

Toutiao

Toutiao or Jinri Toutiao is a Chinese news and information content platform, a core product of the Beijing-based company ByteDance. By analyzing the features of content, users and users’ interaction with content, the company's algorithm models generate a tailored feed list of content for each user. Toutiao is one of China's largest mobile platforms of content creation, aggregation and distribution underpinned by machine learning techniques, with 120 million daily active users as of September 2017. [https://en.wikipedia.org/wiki/Toutiao]

Douban

Douban.com (Chinese: 豆瓣; pinyin: Dòubàn), launched on March 6, 2005, is a Chinese social networking service website that allows registered users to record information and create content related to film, books, music, recent events, and activities in Chinese cities. It could be seen as one of the most influential web 2.0 websites in China. Douban also owns an internet radio station, which ranks No.1 in the iOS App Store in 2012. Douban was formerly open to both registered and unregistered users. For registered users, the site recommends potentially interesting books, movies, and music to them in addition to serving as a social network website such as WeChat, Weibo and record keeper; for unregistered users, the site is a place to find ratings and reviews of media. Douban has about 200 million registered users as of 2013. The site serves pan-Chinese users, and its contents are in Chinese. It covers works and media in Chinese and in foreign languages. Some Chinese authors and critics register their official personal pages on the site. [https://en.wikipedia.org/wiki/Douban]

Content

Weibo realTimeHotSearchList can be regarded as a platform for gathering celebrity gossip, social life and major news. In this document, I collect the top 50 topics of the hot search list every 12 hours during the day, so there are 100 hot topics each day. These topics are converted into English by Google translation, although the translation effect is not ideal due to sentence segmentation and language background deviation. In this document, I created a new column ['Coron-Related ( 1 yes, 0 not ) '] to mark topics related to the new crown, if relevant, it is marked as 1, if not then marked empty or 0. The google translation is extremely inaccurate (so maybe google the Chinese title to confirm is the best bet...

This dataset contains user reviews and comments from the Bumble dating application on the Google Play Store. Bumble is an online dating app where, in heterosexual matches, female users typically initiate the first contact. Beyond romantic connections, Bumble also facilitates finding friends through "BFF mode" and business networking via "Bumble Bizz". This dataset is valuable for understanding user experiences and sentiment towards the app.

Columns

• reviewId: A unique identifier for each user's review.
• userName: The name of the user who posted the review.
• userImage: A URL to the user's profile image.
• content: The textual comment or feedback provided by the user.
• score: The rating given by the user, ranging from 1 to 5.
• thumbsUpCount: The number of 'thumbs up' or likes a specific comment received.
• reviewCreatedVersion: The version number of the app on which the review was created.
• at: The date and time when the review was created.
• replyContent: The content of any reply made by the Bumble company to the user's comment.
• repliedAt: The date and time when the company's reply was posted.

Distribution

The dataset is typically provided as a data file, often in CSV format. It appears to contain a substantial number of records, with reviewId having 168,651 unique values. The data quality is rated as 5 out of 5, and the version of this dataset is 1.0.

Usage

This dataset is ideal for: * Natural Language Processing (NLP) tasks, such as sentiment analysis of user comments. * Market research to gain insights into user satisfaction and preferences regarding dating apps. * Analysing app performance based on user ratings and feedback. * Studying trends in social networks and popular culture related to online dating. * Identifying common user issues or popular features within the Bumble app.

Coverage

The dataset is global in its geographic scope. The reviews span a time period from 29 November 2015 to 28 June 2025. It primarily covers the experiences of Google Play Store users of the Bumble app. As of June 2016, 46.2% of Bumble's users were female.

License

CC-BY

Who Can Use It

• Data scientists and machine learning engineers interested in text analysis and sentiment modelling.
• App developers seeking direct user feedback to improve application features and user experience.
• Researchers focusing on online dating dynamics, social media behaviour, and popular culture.
• Businesses aiming to understand consumer sentiment and competitive landscapes in the social networking and dating industries.

Dataset Name Suggestions

• Bumble Google Play Reviews
• Bumble App User Feedback
• Bumble Play Store Ratings
• Bumble Dating App Reviews Dataset

Attributes

Original Data Source: Bumble Dating App - Google Play Store Review

The dataset contains 1,462 entries and 22 columns, primarily capturing responses from a survey about e-customer relationships in e-commerce. Key demographic information includes age and sex, alongside questions on e-commerce usage patterns, such as daily app usage time and weekly purchase frequency.

The survey assesses factors influencing customer decisions, including the impact of e-commerce promotions (vouchers, coupons, flash sales), app usability, order processing speed, logistics ease, and customer service responsiveness. Further columns explore trust in sellers, the importance of regular order updates, perceived product quality, pricing competitiveness compared to physical stores, and the influence of social media advertisements and famous ambassadors. Additionally, participants rated their confidence in flagship stores, consideration of online shop ratings, and tendency to purchase from well-reviewed stores. Each response is rated on a scale, reflecting the importance of various factors in their e-commerce shopping behaviors.