Demographic-Based Public Perception Analysis of Electric Vehicles on Online Social Networks
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe study is interesting, and although the authors (2 and 3) have a similar study, they cite it appropriately, and this one can be complementary to the previous one. However, I have some major considerations to improve the work and differentiate it from the previously published one.
To further enhance the rigor and impact of this research, several key improvements can be considered:
-Clearly articulate the specific objectives of the study. Define the scope of demographic variables examined and the key aspects of public perception analyzed. This will provide a more focused and targeted investigation.
-Consider incorporating a comparative analysis of different machine learning models to demonstrate the robustness of the results.
- Consider incorporating an analysis of the demographic characteristics considered, such as socioeconomic status.
-Clearly connect the study's findings to practical implications for policymakers and marketers. Propose specific recommendations based on the insights gained, emphasizing how this information can be utilized to enhance both market strategies and future policies promoting EV adoption.
- Suggest avenues for future research based on the gaps identified in the current study. This could involve exploring additional demographic variables, examining evolving trends in public perception, or investigating the impact of specific policy interventions.
- There are a few areas where the conclusions could be further refined:
a) While the mention of the M3 system and topic modeling is informative, providing more details on the specific methodologies used would enhance the transparency and replicability of the study. This could include elaborating on the algorithms employed and the rationale behind their selection.
b) Acknowledging the limitations of the study, such as any biases in the data or potential shortcomings in the classification systems used, would strengthen the integrity of the conclusions. Openly addressing these limitations would contribute to a more nuanced interpretation of the findings.
c) While the conclusions suggest that the results can be used to inform market strategies and policy decisions, offering specific recommendations based on the findings would enhance the practical applicability of the research. This could involve proposing targeted interventions for different demographic groups or outlining potential policy adjustments.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe main feature is that we used machine learning technology to analyze public opinion about electric vehicles in various demographic groups on online social networks (OSNs) Reddit and Twitter.
However, please be sure to faithfully supplement the matters below. If only this part improves, I think it would be a good study worth trying.
It is said that machine learning technology was used, but a specific explanation is needed. There is no sufficient explanation of how and what process machine learning was used. I think it would be good to explain the machine learning process better by dividing it into separate sections.
This means that sections 3.1 and 3.2 are very poorly written.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors1. Introduction
Sufficiently elaborated introduction that clarifies the origins of the problem.
2. Literature review
This chapter is divided into three sub-chapters, which need to be revised and supplemented according to the research objective.
The methodology part is missing.
3. Data
In this chapter, it is necessary to specify more precisely what kind of data it is, for example: in what time frame did you collect it?
3.1. It is very general, it cannot be used
3.1.1. It is not necessary to refer to sources in this section, I suggest that the authors go directly to the description of their own scientific work.
3.1.2. and to clarify in what time frame the original research was done, 2011-2022, or 2011-2020 as stated in the article they refer to?
3.2. Why the authors decided to do pre-processing and clean the collected data up to Age and Gender, and Political Leaning. So they were working with a different data sample? I suggest that they move this chapter between 3.1 and 3.1.1
4.1. figure 3 is mentioned, but absent in the article
4.2. You talk about figure 4, but it is only found in chapter 4.3
4.3. the text is unrelated to the images and the images are unrelated to the text
4.4. the text is unrelated to the images and the images are unrelated to the text
4.5 This chapter should be moved to the discussion section
5. Case Study: Colorado vs. Utah
I don't see the justification in this part. There is no clear line.
6. Discussion
It does not correspond to the methodology, there are no references to images in the text.
The work seems inconsistent.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors have correctly addressed my suggestions, so I have no further suggestions to add. The manuscript can be accepted for publication.
Reviewer 2 Report
Comments and Suggestions for AuthorsThe article has been well improved overall.
Reviewer 3 Report
Comments and Suggestions for AuthorsThe article was fundamentally revised according to the comments.
