1. Ask about something specific on the person's profile Research found that using phrases like "you mention" or "I noticed that", which showed.Users have complained about fake accounts in dating apps and problems with app downloads and uninstalls. Topic 10 with words like 'pay' 'subscription' 'money' '.Though it's a positive to signal an openness to share about yourself with a potential match, this phrase is most often a lazy-sounding stand-in.Did I like using it? The app itself is lovely. Designed beautifully, nice features, makes it easy to see what type of person you would possibly.These principles apply to dating app conversations, too: “You need to keep that rhythm up, you need to keep that pace up,” McQuiston says. “Big.Stuck for an opening line on a dating app? Try out one of these funny and flirty lines to get the conversation going.Here's what you say in this message. “No idea if we'd get along, but your Jurassic Park t-shirt is a step in the right direction.” You could put.So we asked 20 real women to divulge the perfect one-liners, questions, and messages they wish men would send on mobile apps or dating sites. Oh, and by the way.
Federal government websites often end in. The site is secure. All the data are available on figshare. With the continuous development of information technology, more and more people have become to use online dating apps, and the trend has been exacerbated by the COVID pandemic in these years. However, there is a phenomenon that most of user reviews of mainstream dating apps are negative. To study this phenomenon, we have used topic model to mine negative reviews of mainstream dating apps, and constructed a two-stage machine learning model using data dimensionality reduction and text classification to classify user reviews of dating apps. The research results show that: firstly, the reasons for the current negative reviews of dating apps are mainly concentrated in the charging mechanism, fake accounts, subscription and advertising push mechanism and matching mechanism in the apps, proposed corresponding improvement suggestions are proposed by us; secondly, using principal component analysis to reduce the dimensionality of the text vector, and then using XGBoost model to learn the low-dimensional data after oversampling, a better classification accuracy of user reviews can be obtained. We hope These findings can help dating apps operators to improve services and achieve sustainable business operations of their apps. At least million people worldwide use digital dating services every month, a study of Smith and Duggan [ 1 ] found that one in ten Americans has used online dating websites or mobile dating apps; sixty-six percent of online daters have met someone they know through dating websites or apps, and 23 percent have met spouses or long-term partners through these sites or apps. Due to the COVID pandemic since , many activities of people have shifted from offline to online. It has also led to a significant increase in the frequency of online dating app users using them. In other words, dating apps have very good market prospects at present. However, a good market prospect also means that there will be cruel competition among enterprises behind it. For operators of dating apps, one of the key factors in keeping their apps stable against the competitions or gaining more market share is getting positive reviews from as many users as possible. The study of Ye, Law and Gu [ 4 ] found significant relationship between online consumer reviews and hotel business performances. This conclusion can also be applied on apps. For user reviews of apps presented in a textual state, we believe that text mining models can be used to analyze these reviews. Since then, topic models based on LDA have become one of the key research areas of text mining. LDA is very widely used in the commercial fields. For example, Wahyudi and Kusumaningrum [ 8 ] have used an LDA-based topic model to perform sentiment analysis on user reviews of online shopping malls in Indonesia in their study.
We Asked 20 Women: What’s your idea of the perfect first message on a dating app?
Most of the sentences that people speak every day contain some kinds of emotions, such as happiness, satisfaction, anger, etc. We tend to analyze the emotions of sentences according to our experience of language communication. Feldman [ 9 ] thought that sentiment analysis is the task of finding the opinions of authors about specific entities. Operators of dating apps usually collect user feelings and opinions through questionnaires or other surveys within the websites or apps. Therefore, we believe that a feasible method is to first build a suitable model to fit the existing customer opinions that have been classified by sentiment tendency. In this way, the operators can then obtain the sentiment tendency of the newly collected customer opinions through batch analysis of the existing model, and conduct more in-depth analysis as needed. At present, many machine learning and deep learning models can be used to analyze text sentiment which is processed by word segmentation. Sun et al. Aljedani, Alotaibi and Taileb [ 12 ] have explored the hierarchical multi-label classification problem in the context of Arabic and propose a hierarchical multi-label Arabic text classification HMATC model using machine learning methods. The results show that the proposed model was superior to all the models considered in the experiment in terms of computational cost, and its consumption cost is less than that of other evaluation models. Shah et al. Jang et al. In their experiments with several text classification datasets, their proposed method outperformed BERT and GCN alone and was more effective than previous studies reported. However, in practice when the text contains many words or the numbers of texts are large, the word vector matrix will obtain higher dimensions after word segmentation processing. Therefore, we should consider reducing the dimensions of the word vector matrix first. The research of Vinodhini and Chandrasekaran [ 16 ] showed that dimensionality reduction using PCA principal component analysis can make text sentiment analysis more effective. LLE Locally Linear Embedding is a manifold learning algorithm that can achieve effective dimensionality reduction for high-dimensional data. He et al.Currently, there are fewer text mining studies on user reviews of apps that people use every day, but this field has caught the attention of researchers [ 18 ]. Much of the research on dating apps now focuses on psychology and sociology, with minority of studies looking at dating apps from a business perspective. The study by Ranzini, Rosenbaum and Tybur [ 19 ] found that Dutch people are more likely to choose Dutch people as potential partners when using dating apps, while Dutch people with higher education are more likely to choose potential partners with higher education backgrounds when using dating apps. Tran et al. Rochat et al. The results show that Tinder users participating in the study could be reasonably divided into four groups, and the users of each group were different in gender, marital status, depression and usage patterns. Tomaszewska and Schuster [ 22 ] compared perceptions related to sexuality of dating app users and non-dating app users, namely their risky sexual scripts and sexual self-esteem, and their risky and sexually assertive behaviors. Results showed that dating app users had more risky sexual scripts and reported more risky sexual behaviors than non-dating app users. In addition, male dating app users had lower sexual self-esteem and were more accepting of sexual coercion than male non-dating app users. Lenton et al. In some research work, researchers have proposed methods or tools to help operators of apps, websites, hotel etc. Considering that user reviews for apps are valuable for app operators to improve user experience and user satisfaction, but manually analyzing large numbers of user reviews to get useful opinions is inherently challenging, Vu et al. Jha and Mahmoud [ 25 ] proposed a novel semantic approach for app review classification, it can be used to extract user needs from application evaluations, enabling a more efficient classification process and reducing the chance of overfitting. Dalal and Zaveri [ 26 ] proposed a view mining system for binary and fine-grained sentiment classification that can be used for user reviews, and empirical studies show that the proposed system can perform reliable sentiment classification at different granularity levels. Considering that a large number of user reviews need to be explored, analyzed, and organized to better assist website operators in making decisions, Sharma, Nigam and Jain [ 27 ] proposed an aspect-based opinion mining system to classify reviews, and empirically demonstrated the effectiveness of this system. Considering that hotel managers in Bali can gain insight into the perceived state of the hotel through hotel user reviews, Prameswari, Surjandari and Laoh [ 28 ] used text mining methods and aspect-based sentiment analysis in their research to capture hotel user opinions in the form of emotions. As a result, we wish to applying machine learning models on mining user reviews of dating apps. In this way, operators of apps can better manage their user review data and improve their apps more effectively.
https://pbs.twimg.com/media/F4e2XWbaYAAmy6L.jpg40 funny and flirty opening lines to use on dating apps
Considering the increasing popularity of dating apps and the unsatisfactory user reviews of major dating apps, we decided to analyze the user reviews of dating apps using two text mining methods. First, we established a topic model based on LDA to mine the negative reviews of mainstream dating apps, analyzed the main reasons why users give negative reviews, and put forward corresponding improvement suggestions. Next, we built a two-stage machine learning model that combined data dimensionality reduction and data classification, hoping to obtain a classification that can effectively classify user reviews of dating apps, so that app operators can process user reviews more effectively. At present, there are several dating apps that are widely used, such as the famous Tinder and Okcupid. Since most users download these apps from Google Play, we believed that app reviews on Google Play can effectively reflect user feelings and attitudes toward these apps. The data are published on figshare. Also, we promise that the methods of data collection used and its application in our study comply with the terms of the website from which the data originated. At the end of May , we have collected a total of 1,, reviews data. First of all, in order to prevent the impact on the results of text mining, we first carried out text cleaning, deleted symbols, irregular words and emoji expressions, etc. Considering that there may be some reviews from bots, fake accounts or meaningless duplicates among the many reviews, we believed that these reviews can be filtered by the number of likes they get. In order to keep the size of data we finally use not too small, and to ensure the authenticity of the reviews, we compared the two screening methods of retaining reviews with a number of likes greater than or equal to 5 and retaining reviews with a number of likes greater than or equal to
Among all the reviews, there are 25, reviews with 10 or more likes, and 42, reviews with 5 or more likes. In order to maintain a certain generality and generalizability of the results of the topic model and classification model, it is considered that relatively more data is a better choice. Therefore, we selected 42, reviews with a relatively large sample size with a number of likes greater than or equal to 5. In addition, in order to ensure that there are no worthless comments in the filtered comments, such as repeated negative comments from robots, we randomly selected comments for careful reading and found no obvious worthless comments in these reviews. Such a ratio is very shocking. Most of the users who reviewed on Google Play were very dissatisfied with the dating apps they were using. Considering the disparity between positive and negative reviews in dating app user reviews, we believed that we need to establish a topic model based on LDA, hoping to mine the customer opinions contained in the negative reviews. At the same time, we thought it is helpful to build a text emotion classification model that can better match dating app user reviews with ratings in Google Play on a scale of 1 to 5. From the previous visualization results, we can clearly find that in the reviews of users on dating apps in Google Play, negative reviews account for the vast majority. This situation may have a certain impact on the operation of the apps, such as the loss of users or the continuous decline of the reputation of the apps. In order to solve such a problem, we have established a topic model using the implicit Dirichlet distribution to mine the information in the bad reviews of dating apps, in order to analyze the reasons behind the bad reviews and put forward corresponding suggestions that can solve the problem. With the explosive growth of text information, digging its theme composition from massive non-structured text information as the main mode of text information analysis. Topic Models are important type of machine learning algorithm that can discover potential mixed themes from the existing document set, so they are important unsupervised learning tools for inferring knowledge.
User review analysis of dating apps based on text mining
Topic models are also universal ways to understand collections of unstructured text documents, they can be used to automate the screening of large amounts of text data. Once key topics have been identified, text files can be grouped for further analysis. In many research works that also use topic models such as [ 30 — 33 ], the effectiveness of LSA and LDA may be difficult to determine Consider that LDA considers both the distribution of topics by document and the distribution of terms by topic compared to LSA [ 34 ], we have applied Latent Dirichlet assignments. The topic model LDA is a non-supervised machine learning method. It can effectively extract the hidden themes in large-scale document sets and corpus libraries. The dimensional reduction capacity, modeling ability and scalability have made it one of the popular research directions in the field of theme mining in recent years. Dirichlet distribution is a probability distribution of a diverse continuous random variable, which is an extension of Beta Distribution. In Bayesian learning, Dirichlet distribution is often used as a prior distribution of multinomial distributions. The gamma function is [ 35 ]. The underlying Dirichlet assignment aims to represent each document T as a mixed distribution of topics, and each topic as a mixed distribution on dictionary D by modeling, where the input document set T and the number of topics S are given. In addition to infering the topic distribution of words in a document, LDA makes assumptions about how topics and documents are generated. Let sigma represent the mixing ratio of words in S topics, a matrix of size S by D. A complete document can be generated by repeated multiple times, while the entire corpus can be generated by repeated extraction of documents [ 36 ]. In order for the model to obtain better analysis results, we first need to determine the values of the hyperparameters of the topic model based on LDA.
Topic coherence based on word co-occurrence patterns is one of the effective indicators to measure the effect of topic models. Table 1 shows the analysis results of the LDA topic model with the final hyperparameters. In general, the negative reviews of users are mainly concentrated in four aspects. First, they are dissatisfied with the payment amounts in dating apps and problems with the payment and refund processes; secondly, they complained about fake accounts in apps, including fake photos, personal information, and the resulting scams. This question is raised when people use many dating apps, Duguay [ 38 ] has pointed out that this problem exists in Tinder and has caused a certain negative impact; thirdly, they were dissatisfied with the advertising push mechanism and subscription mechanism in apps; finally, they also felt that the matching mechanism provided by apps needed to be improved. In order to establish a text sentiment classification model that can distinguish different reviews in a timely and effective manner, we first cleaned the comment text and perform term frequency—inverse document frequency TF-IDF processing on the data. Looking at the dimensionalities of the training data set, we find that the training dataset is higher than 25, dimensionalities, which is close to the number of samples in the training data set itself. We believed that the high dimensionality of the training dataset would interfere with the accuracy of the classification model. At the same time, as shown in Fig 1 , the proportion of each category in the dataset is very uneven, which may also interfere with the accuracy of the classification model. In addition, we considered the methods of applying XGBoost and LightGBM to learn only oversampled data as baseline models to evaluate the effect of applying dimensionality reduction models and the final effect of two-stage models. Principal Component Analysis PCA is a famous data dimensionality reduction model in multivariate statistical analysis. It extracts the amount of information contained in the original high-dimensional data by linear combination, which is called principal components. According to the variance contribution rate of the extracted principal components, the principal component analysis selects the amount of information required in practical applications to achieve the purpose of reducing the data dimension. Then we can standardize the data according to formula 3 :. The variance of each principal component obtained by the principal component analysis and the information contained in the principal component are decreasing from large to small. We follow the variance contribution rate of each principal component:. Substitute the normalized data for calculating the principal components:. Finally, we get the required dimensionality-reduced data, which is expressed in matrix form as [ 39 ]:.
I, A Single Person, Tried Six Different Dating Apps So That You Don't Have To
Locally Linear Embedding LLE is a method of data dimensionality reduction for the data of nonlinear signal feature vector dimensions. The idea of this dimensionality reduction is not just to reduce the number of dimensions, but to maintain the mathematical properties of the original data. Under the condition that the characteristics remain unchanged, LLE maps the data in the high-dimensional space to the low-dimensional space, and realizes the second extraction of eigenvalues in the data. For each training instance x i , LLE analyzes the k nearest neighbors of the instance, and then tries to reconstruct x i as a linear combination of these neighbors. Therefore, LLE first constructs an unconstrained optimization, where W is a weight matrix containing all weights w i , j , and the second constraint requires that the weights of each training data x i be normalized. Next, LLE will map the training data to a d-dimensional space where dn , while preserving these local relationships as much as possible. In contrast, LLE keeps the weights constant and finds the best location for the data in a low-dimensional space. Here Z is a matrix containing all z i. Before building a classifier to fit the dimensionality-reduced data, we should pay attention to the serious imbalance of classification labels in the data. It can be intuitively found from the previous pie chart Fig 1 that the number of samples with classification labels 2, 3 and 4 is much less than the number of samples with labels 1 and 5. In general, such a situation can easily affect the performance of the classification model. The research of Chawla et al. In order to solve this problem, we have used the SMOTE method to oversample the data when building the classification model, so that the sample size corresponding to each classification label tended to be balanced. XGBoost was proposed by Chen and Guestrin [ 42 ]. At the same time, the loss function of XGBoost performs second-order Taylor expansion on the error part, which is more accurate than the first-order Taylor expansion on the error part in GBDT. XGBoost performs parallel selection on the establishment process of each weak learner in the algorithm, which improves the efficiency of the algorithm. LightGBM was proposed by Ke et al. LightGBM also uses the histogram algorithm. According to the data binning strategy, the nodes of the decision tree can improve the calculation speed when splitting.
The two learning methods it supports feature parallelism and data parallelism further reduce the computational cost. The results of our model are presented in Table 2. Data dimensionality reduction and oversampling using principal component analysis did not improve the classification effect of the LightGBM model much. On the other hand, 0. Moreover, as the data has a total of 5 categories was considered, we believed that this level of accuracy is high enough to be acceptable. We suggest that dating app operators can use this method to establish an XGBoost model on the data after dimensionality reduction by principal component analysis and oversampling, so as to facilitate batch text sentiment classification for customer opinions collected in the future. At the same time, the current payment and refund system should be improved to ensure the timely completion of payment and refund for users. Secondly, we recommend that operators of apps develop more effective ways to identify false information about users. For example, the authenticity of user information can be analyzed by combining multi-modal data such as user photos, IP addresses, and login times. Then, we suggest that app operators improve the current advertising push mechanism and subscription mechanism, and they can develop more effective recommendation system algorithms to meet different user needs. Futhermore, we recommend that the operators of apps improve the dating object matching mechanism, which can start from the distance between users, the ages and living habits of users, etc. Finally, we believe that it is more effective to use principal component analysis to reduce the dimensionality of the text vectors of user reviews of dating apps, and then use the XGBoost model to learn the oversampled low-dimensional data. Such a two-stage machine learning model can efficiently classify these reviews so that operators of apps can batch process the collected user review data. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Jan 18 PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at gro.
3. Americans’ opinions about the online dating environment
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7 of the best ways to start a conversation on a dating app, according to relationship therapists
Please upload your review as an attachment if it exceeds 20, characters. Reviewer 1: It's possible that I'll suggest giving this article a presentation at a conference so that You may have an audience and continue the conversation about the issues. My primary justification is on the fact that "users" are the "unit of analysis" linked with this study; hence, there needs to be a concrete subjective evaluation to generate an accurate construct that is suitable for quantifying the impact of "User Reviews. Generally speaking, user-based aspects used in this study is faulty. You don't need any sort of conceptualised form of an advanced thought in order to gain something new through "Negative Review Mining. To begin, you have to get a firm grasp on the fact that ratings and reviews are inextricably linked, and it is your responsibility to ensure that this connection is maintained. The users review and rating is important because it has led to understanding of the rating approached mapped to reviews. Rating is numeric and reviews are reflections. The proposed four facets of "bad reviews of users" are not utilised in any novel way by Section 3, which does not produce anything that is formulated. The fourth section is a routine analysis, and it does not introduce anything novel that contributes to the advancement of the research field. The XGBoost and LightGBM models are applied to the dataset that is already in existence without any additional information being connected to them. The strengths of the paper are that it is well structured, the description of the related work is well done and that results are extensively compared to results of the similar research. Classifying protein-protein interaction articles from biomedical literature using many relevant features and context-free grammar. PLOS authors have the option to publish the peer review history of their article what does this mean? If published, this will include your full peer review and any attached files. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files. To use PACE, you must first register as a user. Registration is free. Please note that Supporting Information files do not need this step. Thank you very much for your guidance and comments on our work. Please find our itemized responses below and our corrections in the re-submitted files. Comments: In your Methods section, please include additional information about your dataset and ensure that you have included a statement specifying whether the collection method of the collected dataset, and its use for research, complied with the terms and conditions for the website. Response: We have uploaded the dataset to figshare. The URI of our data is:. Comments: Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code and data that underpins the findings in the manuscript. Comments: Thank you for stating the following in the Acknowledgments Section of your manuscript:. Please include your amended statements within your cover letter; we will change the online submission form on your behalf.. Comments: In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. Comments: We note you have included a table to which you do not refer in the text of your manuscript.
Make Online Dating Work! for you
Thank you very much for taking your time to review this manuscript. Please find our itemized responses below. Comments: It's possible that I'll suggest giving this article a presentation at a conference so that You may have an audience and continue the conversation about the issues. Response: The starting point of our research is based on the perspective of enterprise information management, that is, by mining the user reviews of the apps, analyzing how the operators of the apps can improve the apps based on the opinions of users, and trying to develop a method for the operators of the apps to quickly classify the unmarked user reviews collected. Regarding the impact of quantifying user reviews that you mentioned, this may require a large-scale market research to understand how a large number of users perceive these reviews in Google Play and how these reviews influence user choices. In fact, this is indeed a very interesting and exciting topic, and we hope to have the opportunity to collect more suitable data for such research in further research. Comments: You don't need any sort of conceptualised form of an advanced thought in order to gain something new through "Negative Review Mining. Response: We strongly agree that an open mind is indeed a necessary condition for people to accept bad reviews, but in the current era of big data, it is difficult for app operators to rely only on an open mind to mine information from a large number of reviews. Therefore, we hope to efficiently mine information from massive user reviews by applying machine learning models. Comments: Rating is numeric and reviews are reflections. Response: In the current reviews of apps, malicious low-scoring reviews or worthless reviews from bots are always inevitable, and these reviews are difficult to express the general thoughts of app users.There are other comments, perhaps due to their short publication time, that do not receive enough likes, and the value of these comments is difficult to determine in batches. Therefore, in order to control the mining value of the data and ensure that the size of data in the dataset is not too small to affect the fit of the machine learning model, we select comments with more than or equal to 5 likes for analysis. Comments: The fourth section is a routine analysis, and it does not introduce anything novel that contributes to the advancement of the research field. Response: We should admit that the fourth part of our study is only an application to existing methods. First of all, LightGBM and XGBoost are actually very good machine learning classification models and are widely used in machine learning research in various fields. And in the process of application, we obtained But considering that LDA considers both the distribution of topics by document and the distribution of topics over LSA, we chose LDA, as detailed at the end of the first paragraph of section 3. Response: In the process of application XGBoost, we obtained Response: We have already referred to the paper you mentioned and cited it in the introduction section. An invoice for payment will follow shortly after the formal acceptance. If you have any billing related questions, please contact our Author Billing department directly at gro. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact gro. Reviewer 1: After giving the piece my complete attention, I can say that the authors have addressed each and every one of my concerns.
