Long short-term memory (LSTM)-based news classification model

Chen Liu

doi:10.1371/journal.pone.0301835

Peer Review History

Original SubmissionNovember 21, 2023
18 Dec 2023 Decision Letter - Kathiravan Srinivasan, Editor PONE-D-23-38857Long Short-Term Memory (LSTM)-based News Classification ModelPLOS ONE Dear Dr. Liu, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== ACADEMIC EDITOR: Please revise and resubmit your manuscript. If the reviewers have requested citations in the review comments, note that it is not a requirement for publication. ============================== Please submit your revised manuscript by Feb 01 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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In your Methods section, please include additional information about your dataset and ensure that you have included a statement specifying whether the collection and analysis method complied with the terms and conditions for the source of the data. 4. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, all author-generated code must be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. [Note: HTML markup is below. Please do not edit.] 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: Partly Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A Reviewer #2: Yes ****** 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 ****** 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: Yes Reviewer #2: Yes ****** 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: The author proposes an LSTM-based approach to news classification in Chinese. The topic of the study is relevant, however, the research design should be improved. In the current form, the research design does not allow for the evaluation of the relevance of the work. 1. A review of the literature lacks the work of the last few years. The author does not describe the classification models based on the transformer architecture. 2. The description of the dataset should be enriched. What is the distribution of texts between categories? What is the source of texts? 3. The author does not specify how the parameters listed in Table 2 were chosen. 4. What kind of averaging was used for the F1-score? 5. The author does not present the values of accuracy in Table 4-5. 6. In my opinion, the main problem of this work is the absence of baselines. The results of the proposed model should be compared with the results of other models for text classification, including BERT-based models. 7. The author is suggested to add the Error Analysis section for a better understanding of the model limitations. 8. Figures 5-8 are not original. Please specify the source. Reviewer #2: Recommendation: Major Review The paper addresses the task of the Chinese news classification. In this manuscript, the author has proposed a Chinese news classification model using BiLSTM. The author has shown that Bi-LSTM models with F1 score of 94.15% outperform unidirectional models which show F1 score of 93.16%. The preprocessing, model building, hyperparameters chosen have been discussed in detail to explain the nuances of the work done. Comments 1) The author has mentioned the dataset is curated but did not explain how it is labeled and validated. It is critical to evaluate the curated dataset to effectively train and test models. Certain statistical and empirical methods should be employed to evaluate the dataset. The link to curated dataset should also be made available to the research community. 2) While the paper uses word2vec embedding and explains it, other embeddings like the BERT Chinese should also be used and the performance compared. 3) Although the paper claims around one percent performance improvement in proposed Bi-LSTM model over unidirectional LSTM, the complexity of the proposed model should be studied and discussed. 4) The performance of the proposed model should be compared with existing approaches on multiple datasets to give thorough comparison. 5) The paper is written in simple English and readable, but mostly highlights basic explanations. It can be improved by including observational insights. ****** 6. 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 Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. 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.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. 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Revision 1
5 Mar 2024 Author Response Dear editors and reviewers, Thank you very much for your comments and professional advice. These opinions help to improve academic rigor of the article. Based on your suggestion and request, we have made corrected modifications on the revised manuscript. We hope that our work can be improved again. Furthermore, we would like to show the details as follows: Reviewer #1 1. A review of the literature lacks the work of the last few years. The author does not describe the classification models based on the transformer architecture. The author’s answer: We have added a literature reference on the classification model of Transformer architecture according to your suggestion, and hope to meet the requirements now. Please see page 7 of the revised manuscript, lines 147–149. 2. The description of the dataset should be enriched. What is the distribution of texts between categories? What is the source of texts? The author’s answer: We agree and have updated. Please see page 7 of the revised manuscript, lines 168–169. 3. The author does not specify how the parameters listed in Table 2 were chosen. The author’s answer: We agree with the reviewer 's opinion that the parameter selection in Table 2 will be more helpful. However, we believe that too detailed description of how to select parameters will make the description of the manuscript redundant. And the choice of parameters can be obtained in the model principle. For these reasons, we choose not to do this modification. 4. What kind of averaging was used for the F1-score? The author’s answer: We have added a literature reference on the classification model of Transformer architecture according to your suggestion, and hope to meet the requirements now. Please see page 24 of the revised manuscript, lines 545–547. 5. The author does not present the values of accuracy in Table 4-5. The author’s answer: We thank the reviewer for pointing this out. We have revised. Please see page 25 of the revised manuscript, line 570 and line 580. 6. In my opinion, the main problem of this work is the absence of baselines. The results of the proposed model should be compared with the results of other models for text classification, including BERT-based models. The author’s answer: This observation is correct. We have changed. Please see page 27 of the revised manuscript, lines 612–632. 7. The author is suggested to add the Error Analysis section for a better understanding of the model limitations. The author’s answer: We agree with the reviewer 's opinion that it would be more helpful to use new data to further elaborate on this point. However, we believe that taking into account the tight time, as well as our argument will not have a better effect. For these reasons, we choose not to make this modification. 8. Figures 5-8 are not original. Please specify the source. The author’s answer: We thank the reviewer for pointing this out. We have revised. Please see page 15 of the revised manuscript, line353. Reviewer #2 1.The author has mentioned the dataset is curated but did not explain how it is labeled and validated. It is critical to evaluate the curated dataset to effectively train and test models. Certain statistical and empirical methods should be employed to evaluate the dataset. The link to curated dataset should also be made available to the research community. The author’s answer: The curated dataset of the study was submitted with the manuscript at the time of submission. 2.While the paper uses word2vec embedding and explains it, other embeddings like the BERT Chinese should also be used and the performance compared. The author’s answer: This observation is correct. We have changed. Please see page 27 of the revised manuscript, lines 612–632. 3.Although the paper claims around one percent performance improvement in proposed Bi-LSTM model over unidirectional LSTM, the complexity of the proposed model should be studied and discussed. The author’s answer: We agree with the reviewer 's opinion that it is more helpful to explain the complexity of the model at this point. However, because of the rush of time and the results proved in the existing literature. For these reasons, we choose not to make this modification. 4.The performance of the proposed model should be compared with existing approaches on multiple datasets to give thorough comparison. The author’s answer: We agree with the reviewer 's opinion. However, because the number of data is relatively small, it may be difficult to obtain the desired results. For this reason, we choose not to make this modification. 5.The paper is written in simple English and readable, but mostly highlights basic explanations. It can be improved by including observational insights. The author’s answer: We appreciate the reviewer’s insightful suggestion and agree that it would be useful. However, such observational insights are beyond the our writing description ability, which aims only to show that the Bi-LSTM models is better. For this reason, we chose not to make this change. 6. 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. The author’s answer: The PLOS ONE journal has a choice when submitting a manuscript that can be made public on a web page in advance for others to refer to. We did not tick this choice when submitting, so we do not need this work. We would like to thank the reviewers again for taking the time to review our manuscript. Yours sincerely, Chen Liu 30 January,2024 Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0301835.r002
25 Mar 2024 Decision Letter - Muhammad Usman Tariq, Editor Long Short-Term Memory (LSTM)-based News Classification Model PONE-D-23-38857R1 Dear Dr. Liu, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Dr. Muhammad Usman Tariq PFHEA, CFCIPD, CMBE, SFSEDA Academic Editor PLOS ONE Additional Editor Comments: The revision has addressed the comments from all reviewers from the previous review. Manuscript is presented in intelligent fashion. However, you need to use the PLOS One template. All figures need to be high quality. Currently some are blur, once you submit the final version, use the pace tool to confirm the eligibility of figures. https://doi.org/10.1371/journal.pone.0301835.r003
Formally Accepted
2 Apr 2024 Acceptance Letter - Muhammad Usman Tariq, Editor PONE-D-23-38857R1 PLOS ONE Dear Dr. Liu, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Muhammad Usman Tariq Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0301835.r004

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