All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Thank you for your submission, and good luck for your future research.
[# PeerJ Staff Note - this decision was reviewed and approved by Rong Qu, a PeerJ Computer Science Section Editor covering this Section #]
The revisions are satisfactory. No further comments.
The revisions are satisfactory. No further comments.
The revisions are satisfactory. No further comments.
The revisions are satisfactory. No further comments.
the comments are addressed
the comments are addressed
the comments are addressed
the comments are addressed
Your manuscript has been reviewed by the experts in the field and they are suggesting a lots of improvements, therefore you are requested to make major changes in the paper and resubmit for consideration.
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
The authors in this work presented a Deep-learning based classification model that utilizes Arabic tweet data to categorize users as potential suicides. The paper is well written and structured. One of my main concerns is the novelty of the work. The authors should carefully address all the following concerns, especially the comments 4—7:
Please see the additional comments section of the review.
Please see the additional comments section of the review.
1. In the abstract, authors should provide a highlight (in 2 to 3 sentences) about the existing landscape (i.e., available related work) and their limitations with a brief justification of the proposed work
2. Line 32-33 (introduction Section Page 1): The statement “More than 300 million individuals are impacted globally, accounting for 4.4% of the global population (WHO), and the number is growing every day” should have a reference.
a. In general, every indicated result or data related to some study should be properly referenced.
b. Example: (line 40 Page 1) “Every year, about 800,000 individuals commit suicide.” Without any reference is not acceptable.
3. Page 2, lines: 90-91: the statement ” However, the traditional ML technique’s performance was restricted as the number of correlations rose dramatically due to the rise in data amount Rao et al. (2020)”. The authors should provide the rationale in terms of the amount of data with certain quantification (i.e., how much it should be?) for the nature of data it is suitable for.. etc
a. A motivational example related to the justification or rationale of using DL instead of traditional ML techniques should be provided.
4. My main concerns are the novelty of the work. In the introduction section, the authors must mention the existing tweet classification techniques (both MPO and DL based) and should JUSTIFY how their approach is novel. Without justifying novelty, In the current form, this work looks like just another study and lacks the merit of a research paper with respect to the novelty (should be based on some novel aspect in the knowledge domain).
5. Related work section should be extended to add more techniques and the recent ones from the year 2021 and year 2022.
6. The comparison of the related work technique is missing. Add a comparison table at the end of the related work section and especially highlight the potential demerits of the existing approach in that table.
7. Based on the related work comparison table (as suggested in point 6), add a research gap statement highlighting the potential shortcoming of the existing approaches so that the proposed solution could be justifiable and be considered novel.
8. “Twint tool”, add a reference to this python tool.
9. The authors collected tweet data using Arabic-related words equivalent to “suicide”. Which other keywords could also be helpful and were not used such as Bipolar disorder, self-killing, lethal, etc. Please add relevant discussion.
10. Equations (1 to 11) should have references if taken from the literature.
11. Algorithm 1 (on page 9) is just a sequence of function calls and does not show the flow of the relevant data items. The appropriate data structures, parameters, and return values should be made part of Algorithm 1 and the mentioned function calls.
12. Results discussion is too brief. The table and figures related to results should have a more relevant discussion special the reasons or rationale for the attainment of better and poorer performances.
13. Table 13 and Figure 6 present redundant result data. Use wither table or figure to depict these results.
This paper presents an optimized deep learning approach for suicide detection through Arabic tweets. The authors used the data from Twitter of Arabic tweets to classify whether a tweet is categorized as a normal or a suicide case. Some of my questions are
You used the five annotators to annotate the data, who were they? did they annotate the data automatically or manually?
Which State-of-the-art performance metrics are reported, please mention those (names) in abstract
An evaluation discussion is needed in the abstract.
Line 109: What follows is a summary of the rest of the paper: In Section 2, the related studies are presented. 110 Section 3 discusses section numbers are missing throughout the manuscript
A very relevant and state of the art study published on similar topic may also be discussed in the related work section
https://www.sciencedirect.com/science/article/abs/pii/S0736585320300046
many grammatical mistakes could be corrected . e.g. line 164-165 says
Table 2 show 5 samples from the retrieved records.
It could be
Table 2 shows five samples from the retrieved records.
Line 187, you may use some appropriate word in replacement of appliance
Line 192, there is no sixth row in table 2, (e.g., the sixth row in Table 2).
The authors mentioned the manual annotation, is it not a tediuous task for large data sets ?, chould you find an automatic way of annotations? Because there are some tools available.
How did you performed Stemming and Lemmatization , stop words removal on Arabic , again manually ? or through some automated tools ?
Regarding table 5 the model USE and BERT are for Arabic language as well ? or did you custimazied them?
Algorithm 1 could be written in more professional way
Line 256, The current section presents could be more appropriately written as This section……
It is recommended to add a comparisons and evaluation metrics before the conclusion section
This paper presents an optimized deep learning approach for suicide detection through Arabic tweets. The authors used the data from Twitter of Arabic tweets to classify whether a tweet is categorized as a normal or a suicide case. Some of my questions are
You used the five annotators to annotate the data, who were they? did they annotate the data automatically or manually?
Which State-of-the-art performance metrics are reported, please mention those (names) in abstract
An evaluation discussion is needed in the abstract.
Line 109: What follows is a summary of the rest of the paper: In Section 2, the related studies are presented. 110 Section 3 discusses section numbers are missing throughout the manuscript
A very relevant and state of the art study published on similar topic may also be discussed in the related work section
https://www.sciencedirect.com/science/article/abs/pii/S0736585320300046
many grammatical mistakes could be corrected . e.g. line 164-165 says
Table 2 show 5 samples from the retrieved records.
It could be
Table 2 shows five samples from the retrieved records.
Line 187, you may use some appropriate word in replacement of appliance
Line 192, there is no sixth row in table 2, (e.g., the sixth row in Table 2).
The authors mentioned the manual annotation, is it not a tediuous task for large data sets ?, chould you find an automatic way of annotations? Because there are some tools available.
How did you performed Stemming and Lemmatization , stop words removal on Arabic , again manually ? or through some automated tools ?
Regarding table 5 the model USE and BERT are for Arabic language as well ? or did you custimazied them?
Algorithm 1 could be written in more professional way
Line 256, The current section presents could be more appropriately written as This section……
It is recommended to add a comparisons and evaluation metrics before the conclusion section
This paper presents an optimized deep learning approach for suicide detection through Arabic tweets. The authors used the data from Twitter of Arabic tweets to classify whether a tweet is categorized as a normal or a suicide case. Some of my questions are
You used the five annotators to annotate the data, who were they? did they annotate the data automatically or manually?
Which State-of-the-art performance metrics are reported, please mention those (names) in abstract
An evaluation discussion is needed in the abstract.
Line 109: What follows is a summary of the rest of the paper: In Section 2, the related studies are presented. 110 Section 3 discusses section numbers are missing throughout the manuscript
A very relevant and state of the art study published on similar topic may also be discussed in the related work section
https://www.sciencedirect.com/science/article/abs/pii/S0736585320300046
many grammatical mistakes could be corrected . e.g. line 164-165 says
Table 2 show 5 samples from the retrieved records.
It could be
Table 2 shows five samples from the retrieved records.
Line 187, you may use some appropriate word in replacement of appliance
Line 192, there is no sixth row in table 2, (e.g., the sixth row in Table 2).
The authors mentioned the manual annotation, is it not a tediuous task for large data sets ?, chould you find an automatic way of annotations? Because there are some tools available.
How did you performed Stemming and Lemmatization , stop words removal on Arabic , again manually ? or through some automated tools ?
Regarding table 5 the model USE and BERT are for Arabic language as well ? or did you custimazied them?
Algorithm 1 could be written in more professional way
Line 256, The current section presents could be more appropriately written as This section……
It is recommended to add a comparisons and evaluation metrics before the conclusion section
This paper presents an optimized deep learning approach for suicide detection through Arabic tweets. The authors used the data from Twitter of Arabic tweets to classify whether a tweet is categorized as a normal or a suicide case. Some of my questions are
You used the five annotators to annotate the data, who were they? did they annotate the data automatically or manually?
Which State-of-the-art performance metrics are reported, please mention those (names) in abstract
An evaluation discussion is needed in the abstract.
Line 109: What follows is a summary of the rest of the paper: In Section 2, the related studies are presented. 110 Section 3 discusses section numbers are missing throughout the manuscript
A very relevant and state of the art study published on similar topic may also be discussed in the related work section
https://www.sciencedirect.com/science/article/abs/pii/S0736585320300046
many grammatical mistakes could be corrected . e.g. line 164-165 says
Table 2 show 5 samples from the retrieved records.
It could be
Table 2 shows five samples from the retrieved records.
Line 187, you may use some appropriate word in replacement of appliance
Line 192, there is no sixth row in table 2, (e.g., the sixth row in Table 2).
The authors mentioned the manual annotation, is it not a tediuous task for large data sets ?, chould you find an automatic way of annotations? Because there are some tools available.
How did you performed Stemming and Lemmatization , stop words removal on Arabic , again manually ? or through some automated tools ?
Regarding table 5 the model USE and BERT are for Arabic language as well ? or did you custimazied them?
Algorithm 1 could be written in more professional way
Line 256, The current section presents could be more appropriately written as This section……
It is recommended to add a comparisons and evaluation metrics before the conclusion section
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.