The number of users in the 'Online Dating' segment of the eservices market in the United States was forecast to continuously increase between 2024 and 2028 by in total 5.3 million users (+8.76 percent). After the ninth consecutive increasing year, the indicator is estimated to reach 65.86 million users and therefore a new peak in 2028. Notably, the number of users of the 'Online Dating' segment of the eservices market was continuously increasing over the past years.Find further information concerning revenue in the United States and revenue growth in Indonesia. The Statista Market Insights cover a broad range of additional markets.

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

Online dating services have increased in popularity around the world, but a lack of quality data hinders our understanding of their role in family formation. This paper studies the effect of online dating services on marital sorting, using a novel dataset with verified information on people and their spouses. Estimates based on matching techniques suggest that, relative to other spouse search methods, online dating promotes marriages that exhibit weaker sorting along occupation and geographical proximity but stronger sorting along education and other demographic traits. Sensitivity analysis, including the Rosenbaum Bounds approach, suggests that online dating's impact on marital sorting is robust to potential selection bias.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13364933%2F23694fae55e2e76299358693ba6f32b9%2Flv-share.jpg?generation=1684843825246772&alt=media" alt=""> ➡️ There are total 3 datasets containing valuable information. ➡️ Understand people's fame and behavior's on a dating app platform. | Column Name | Description | |---------------------|------------------------------| | Age | The age of the user. | | Number of Users | The total number of users. | | Percent Want Chats | Percentage of users who want chats. | | Percent Want Friends| Percentage of users who want friendships. | | Percent Want Dates | Percentage of users who want romantic dates. | | Mean Kisses Received| Average number of kisses received by users. | | Mean Visits Received| Average number of profile visits received by users. | | Mean Followers | Average number of followers for each user. | | Mean Languages Known| Average number of languages known by users. | | Total Want Chats | Total count of users interested in chats. | | Total Want Friends | Total count of users looking for friendships. | | Total Want Dates | Total count of users seeking romantic dates. | | Total Kisses Received| Overall count of kisses received by users. | | Total Visits Received| Overall count of profile visits received by users. | | Total Followers | Overall count of followers for all users. | | Total Languages Spoken| Total count of languages spoken by all users. |

SUMMARY

When Dating apps like Tinder were becoming viral, people wanted to have the best profile in order to get more matches and more potential encounters. Unlike other previous dating platforms, those new ones emphasized on the mutuality of attraction before allowing any two people to get in touch and chat. This made it all the more important to create the best profile in order to get the best first impression.

Parallel to that, we Humans have always been in awe before charismatic and inspiring people. The more charismatic people tend to be followed and listened to by more people. Through their metrics such as the number of friends/followers, social networks give some ways of "measuring" the potential charisma of some people.

In regard to all that, one can then think:

what makes a great user profile ? how to make the best first impression in order to get more matches (and ultimately find love, or new friendships) ? what makes a person charismatic ? how do charismatic people present themselves ? In order to try and understand those different social questions, I decided to create a dataset of user profile informations using the social network Lovoo when it came out. By using different methodologies, I was able to gather user profile data, as well as some usually unavailable metrics (such as the number of profile visits).

Content

The dataset contains user profile infos of users of the website Lovoo.

The dataset was gathered during spring 2015 (april, may). At that time, Lovoo was expanding in european countries (among others), while Tinder was trending both in America and in Europe. At that time the iOS version of the Lovoo app was in version 3.

Accessory image data The dataset references pictures (field pictureId) of user profiles. These pictures are also available for a fraction of users but have not been uploaded and should be asked separately.

The idea when gathering the profile pictures was to determine whether some correlations could be identified between a profile picture and the reputation or success of a given profile. Since first impression matters, a sound hypothesis to make is that the profile picture might have a great influence on the number of profile visits, matches and so on. Do not forget that only a fraction of a user's profile is seen when browsing through a list of users.

https://s1.dmcdn.net/v/BnWkG1M7WuJDq2PKP/x480

Details about collection methodology In order to gather the data, I developed a set of tools that would save the data while browsing through profiles and doing searches. Because of this approach (and the constraints that forced me to develop this approach) I could only gather user profiles that were recommended by Lovoo's algorithm for 2 profiles I created for this purpose occasion (male, open to friends & chats & dates). That is why there are only female users in the dataset. Another work could be done to fetch similar data for both genders or other age ranges.

Regarding the number of user profiles It turned out that the recommendation algorithm always seemed to output the same set of user profiles. This meant Lovoo's algorithm was probably heavily relying on settings like location (to recommend more people nearby than people in different places or countries) and maybe cookies. This diminished the number of different user profiles that would be pr...

Abstract

How Couples Meet and Stay Together (HCMST) is a study of how Americans meet their spouses and romantic partners.

• The study is a nationally representative study of American adults.
• 4,002 adults responded to the survey, 3,009 of those had a spouse or main
  romantic partner.
• The study oversamples self-identified gay, lesbian, and bisexual adults
• Follow-up surveys were implemented one and two years after the main survey, to study couple dissolution rates. Version 3.0 of the dataset includes two follow- up surveys, waves 2 and 3.
• Waves 4 and 5 are provided as separate data files that can be linked back to the main file via variable caseid_new.

The study will provide answers to the following research questions:

1. Do traditional couples and nontraditional couples meet in the same way? What kinds of couples are more likely to have met online?
2. Have the most recent marriage cohorts (especially the traditional heterosexual same-race married couples) met in the same way their parents and grandparents did?
3. Does meeting online lead to greater or less couple stability?
4. How do the couple dissolution rates of nontraditional couples compare to the couple dissolution rates of more traditional same-race heterosexual couples?
5. How does the availability of civil union, domestic partnership or same-sex marriage rights affect couple stability for same-sex couples? This study will provide the first nationally representative data on the couple dissolution rates of same-sex couples.

Methodology

Universe:

The universe for the HCMST survey is English literate adults in the U.S.

**Unit of Analysis: **

Individual

**Type of data collection: **

Survey Data

**Time of data collection: **

Wave I, the main survey, was fielded between February 21 and April 2, 2009. Wave 2 was fielded March 12, 2010 to June 8, 2010. Wave 3 was fielded March 22, 2011 to August 29, 2011. Wave 4 was fielded between March and November of 2013. Wave 5 was fielded between November, 2014 and March, 2015. Dates for the background demographic surveys are described in the User's Guide, under documentation below.

Geographic coverage:

United States of America

Smallest geographic unit:

US region

**Sample description: **

The survey was carried out by survey firm Knowledge Networks (now called GfK). The survey respondents were recruited from an ongoing panel. Panelists are recruited via random digit dial phone survey. Survey questions were mostly answered online; some follow-up surveys were conducted by phone. Panelists who did not have internet access at home were given an internet access device (WebTV). For further information about how the Knowledge Networks hybrid phone-internet survey compares to other survey methodology, see attached documentation.

The dataset contains variables that are derived from several sources. There are variables from the Main Survey Instrument, there are variables generated from the investigators which were created after the Main Survey, and there are demographic background variables from Knowledge Networks which pre-date the Main Survey. Dates for main survey and for the prior background surveys are included in the dataset for each respondent. The source for each variable is identified in the codebook, and in notes appended within the dataset itself (notes may only be available for the Stata version of the dataset).

Respondents who had no spouse or main romantic partner were dropped from the Main Survey. Unpartnered respondents remain in the dataset, and demographic background variables are available for them.

**Sample response rate: **

Response to the main survey in 2009 from subjects, all of whom were already in the Knowledge Networks panel, was 71%. If we include the the prior initial Random Digit Dialing phone contact and agreement to join the Knowledge Networks panel (participation rate 32.6%), and the respondents’ completion of the initial demographic survey (56.8% completion), the composite overall response rate is a much lower .326*.568*.71= 13%. For further information on the calculation of response rates, and relevant citations, see the Note on Response Rates in the documentation. Response rates for the subsequent waves of the HCMST survey are simpler, using the denominator of people who completed wave 1 and who were eligible for follow-up. Response to wave 2 was 84.5%. Response rate to wave 3 was 72.9%. Response rate to wave 4 was 60.0%. Response rate to wave 5 was 46%. Response to wave 6 was 91.3%. Wave 6 was Internet only, so people who had left the GfK KnowledgePanel were not contacted.

**Weights: **

See "Notes on the Weights" in the Documentation section.

Usage

When you use the data, you agree to the following conditions:

1. I will not use the data to identify individuals.
2. I will not charge a fee for the data if I distribute it to others.
3. I will inform the contact person abo

Most people seek to establish romantic or intimate relationships in life, including people with mental health problems. However, this has been a neglected topic in mental health practice and research. This study aimed to investigate views of mental health and social care staff about the appropriateness of helping service users with romantic relationships, barriers to doing this, and suggestions for useful ways to support this. An online survey comprising both closed, multiple response and free-text questions was circulated to mental health organisations across the U.K. via social media, professional networks and use of snowballing sampling. A total of 63 responses were received. Quantitative data were analysed using descriptive statistics, and are reported as frequencies and percentages. Qualitative data were interpreted using thematic analysis, using an inductive approach. Although most participants reported that ‘finding a relationship’ conversations were appropriate in their job role, many barriers to supporting service users were identified, including: a lack of training; concerns about professional boundaries; concerns about service user capacity and vulnerability; and concerns about being intrusive. Participant suggestions for future support included educating service users on safe dating behaviours, and practical interventions such as assisting service users to use dating sites and engage with social activities to develop social skills and meet others. Staff were willing to help service users seek an intimate relationship but may need specific training or guidance to facilitate this confidently and safely. This study elucidates the need for further research in this area, particularly in understanding service user perspectives, and in developing resources to support staff in this work.

What’s Included

This training dataset comprises more than 10,000 conversational text data between two native English people in the general domain. We have a collection of chats on a variety of different topics/services/issues of daily life, such as music, books, festivals, health, kids, family, environment, study, childhood, cuisine, internet, movies, etc., and that makes the dataset diverse.

These chats consist of language-specific words, and phrases and follow the native way of talking which makes the chats more information-rich for your NLP model. Apart from each chat being specific to the topic, it contains various attributes like people's names, addresses, contact information, email address, time, date, local currency, telephone numbers, local slang, etc too in various formats to make the text data unbiased.

These chat scripts have between 300 and 700 words and up to 50 turns. 150 people that are a part of the FutureBeeAI crowd community contributed to this dataset. You will also receive chat metadata, such as participant age, gender, and country information, along with the chats. Dataset applications include conversational AI, natural language processing (NLP), smart assistants, text recognition, text analytics, and text prediction.

This dataset is being expanded with new chats all the time. We are able to produce text data in a variety of languages to meet your unique requirements. Check out the FutureBeeAI community for a custom collection.

This training dataset's licence belongs to FutureBeeAI!

We tested the existence of a rejection mind-set in online dating across three studies. In Study 1, we presented people with pictures of hypothetical partners, to test if and when people’s general choice behavior would change. In Study 2, we presented people with pictures of partners that were actually available and tested the gradual development of their choice behaviors as well as their success rate in terms of mutual interest (i.e., matches). In Study 3, we explored potential underlying psychological mechanisms. Specifically, and in line with choice overload literature, we explored whether the rejection mind-set may be due to people experiencing lower choice satisfaction and less success over the course of online dating. As an additional goal, we explored the potential moderating role of gender.

Context

In an era where technology plays a significant role in people’s lives, one cannot deny that it changes the way people interact and communicate with others. Today, technology has caused some significant changes in the dating world as well. Online dating is a new trend that is influencing many people around the world.

As a data scientist, you are required to predict the match percentage between the users in a matrix format based on the attributes provided by the user on a dating website.

Note

Based on the user’s sexual orientation, you are required to perform the following:

1. If a user is heterosexual (prefers the opposite sex), then the match percentage must be 0 for this user with respect to other users of the same gender if the other users have the same behavior.
2. f a user is a homosexual (prefers the same sex), then the match percentage must be 0 for this user with respect to other users of the opposite gender if the other users have the same behavior.
3. The match percentage of a user with her/himself must be zero.

Content

The data is of a dating site that describes the user from various attributes like sex, orientation, relationship status, smokes or not, languages known, etc.

Acknowledgements

This is a competition on HackerEarth. https://www.hackerearth.com/problem/machine-learning/predict-the-match-percentage-25-818cf487/

Inspiration

The idea is to find the match percentage between each user with another user. Also, consider the note point in a context.

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