PONE-D-22-30174Analysis of Dating Apps' User Reviews Based on Text MiningPLOS ONE

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 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." However, while employing machine learning, the assumption is that a new course of action "or knowledge" can emerge from the analysis of the machine learning, sadly the work has not showed any improvement on the problems linked with "User Reviews" on dating apps. 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." Instead, all you need is to have an open mind.

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 numeric values of rating will indicate if DATIN APPS is Good or BAD, whereas reviews will indicate the user’s perceptions. Previous research reveals that “Bad ratings are trustworthy regardless of the number of reviews”, that is users tend to believe reported bad rating. On the other hand, “Good ratings are trustworthy only when they come along with a high number of reviews”

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.

,

Reviewer #2: In this paper, the authors are proposed “Analysis of Dating Apps’ User Reviews Based on Text Mining”.

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.

Minor revisions:

1. Authors should draw a graphical abstract of the proposed approach

2. Authors should mention the names of the all the six dating apps

3. Why authors used LDA instead of LSA.

4. Why authors used XGBoost justify it

5. Proofread the entire manuscript

6. Authors follow the recently published paper “Abdulkadhar, S., Murugesan, G., & Natarajan, J. (2020). Classifying protein-protein interaction articles from biomedical literature using many relevant features and context-free grammar. Journal of King Saud University-Computer and Information Sciences, 32(5), 553-560.”

7. Proofread the entire manuscript.

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Reviewer #1: No

Reviewer #2: No

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User Review Analysis of Dating Apps based on Text Mining

PONE-D-22-30174R1

Dear Dr. Chen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Viacheslav Kovtun, Dr.Sc., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: After giving the piece my complete attention, I can say that the authors have addressed each and every one of my concerns.

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7. 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.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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.

Reviewer #1: No

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