XLNet-Caps: Personality Classification from Textual Posts
, Jiazhuang Zheng 1,2, Qing Li 1,2, Chenglong Wang 1,2, Hanyun Zhang 1,2 and Jibing Gong 1,2,3,*Round 1
Reviewer 1 Report
Please see the following detailed comments:
- It would be nice if the authors could include a hyper-parameter selection process for the network layers in 3.5. Such a process may include a manual grid search over a combination of hyper parameters. The selected combination should be the one that outperforms others on most of the datasets.
- The authors may want to add the performance of XLNet without a capsule network in Table 3 and 4.
- A more detailed description of the datasets is needed, such as the size of training/testing sets for each dataset.
- Multiple-authored articles should be correctly referenced in the bibliography. For example, reference 5-9 and some others should have all the authors’ names spelled out.
Author Response
Dear reviewer:
Thank you for your suggestions on the correction of the paper，I replied to your question in the attachment, please check it out, thank you
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This paper deals with aXLNet-Caps: Personality Classification from Textual Posts. I think this paper well described previous work and is also well organized. However, I would like to point out the following.
- It is not clear what is differentiation compare to other papers even though the authors proposed the model, XLNet-Caps.
- In section 3.2, Text Modeling Based on Time Series. what's the mean of the time series? It is not clear. Thus authors should explain it in section 3.2
- Please add the following reference if it is related.
Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network Jihad Anwar Qadir, Abdulbasit K. Al-Talabani, and Hiwa A. Aziz International Journal of Fuzzy Logic and Intelligent Systems 2020;20(4):272-277
Author Response
Dear reviewer:
Thank you for your suggestions on the correction of the paper，I replied to your question in the attachment, please check it out, thank you.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The authors have addressed all the concerns in the revision.
Reviewer 2 Report
I think this paper well revised according to the reviewer's point out. Thus I would like to decide as an "accept"
