How Do Visitors to Mountain Museums Think? A Cross-Country Perspective on the Sentiments Decoded from TripAdvisor Reviews

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Major comments

If the focus of the paper is on the difference between local and non-local visitors, then this should be emphasized in the introduction rather than Trip Advisor data sources. The same is for the literature review. Although all research questions refer to differences between visitors regarding their language and demographics, very little background is presented in the literature review. In this way, it is difficult to assess how this research corroborates with previews works. A section with a literature review about local and non-local visitors to museums is mandatory to add. This results in another problem, which occurs in the last part of the paper. In the discussion and conclusion sections author did not refer their findings to previous works. All scientific papers should be placed within a course of research, build on preview works and contribute to scientific discourse.

The authors actually did not explain why mountain museums were chosen as a research area. Why exactly were those museums selected? In the method section authors did not refer to previous works. Therefore, it is not clear how this process differs or is in line with previous research. For example, authors set a character threshold at 50. Why 50? Which threshold was set in previous research? Etc.

Please explain how you identify the visitor type.

Minor comments

Under Table 3, there is a reference "1 Based on a dataset" where the source is missing. 

Author Response

Please, see the attachment. All edits were performed by using Track Changes. 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper presents an interesting analysis of visitor sentiments towards mountain museums using TripAdvisor reviews. It combines topic modeling and sentiment analysis in a quest to compare local vs. non-local perception and different visitor profiles.
The paper is well structured, the research questions are well defined and the use of LDA is suitable for analysing large volumes of textual data and extracting meaningful insights. The study provides practical recommendations for museum management, such as tailoring content to different visitor segments and improving accessibility and interactivity. The state of the art is well documented also.

Several shortcomings of the paper include:

The use of a single review website (TripAdvisor), which has a broad coverage of various subjects and may not be well suited for museums. Nevertheless, the large number of reviews provides a good source of information.

Other demographic information could lead to different patterns, generally useful for museums, such as: age, gender, and education level.

The keyword cloud could be better explained and the selection performed more rigorously.

How were the museums selected?

Can you provide more details on the parameters used for LDA topic modeling, such as the number of topics and the convergence criteria?

Although the English is generally good, please check for some typos, such as: “Given it vast repository of user-generated”

Author Response

Please, see the attachment. All changes were made performed by using Track Changes. 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Areas to Improve

1. Intro/Lit Review: Set the Stage Better  
 Connect the dots: Link your work more tightly to niche tourism and mountain heritage research. Why are mountain museums a unique beast? Are there quirks (e.g., remote locations, alpine culture) that set them apart from city museums? Help readers see why this niche matters!  
 Show your “why”: You mention Riva & Agostino (2022), but how does your work push further? Is it the mountain focus? The visitor profiling? Spell out your “aha” moment—don’t make readers guess!  

2. Methods: Fill in the Blanks  
 Spill the beans on your LDA setup: How many topics did you test? Did you use coherence scores to pick the best model? Nerdy details matter—it’s like sharing the recipe so others can bake the same cake!  
 Mind the ethics: Did you anonymize reviews? Did TripAdvisor’s terms allow scraping? A quick line here reassures folks you played by the rules.  
 Prove your sentiment chops: TextBlob is handy, but how well does it handle nonEnglish reviews? Did you spotcheck accuracy? A table or footnote saying “We validated 100 random reviews manually” would ease skeptics.  

3. Results: Dig Deeper  
 Solve the business traveler mystery: They’re super satisfied but timecrunched—why? Is it killer highlights tours? A “TL;DR” exhibit design? Connect the dots between efficiency and joy.  
 Talk about language hurdles: Nonlocal reviews = multiple languages. Did you translate them? How? Did emojis or slang trip up your analysis? Be real about the messy bits!  

4. Discussion/Conclusion: Flex Those Insights  
 Show off your theoretical muscles: Don’t just report findings—tell us how your LDA approach cracks open crosscultural tourism research. Are you setting a new standard?  
 Join the bigger conversation: How do your results challenge the “expert vs. visitor” power dynamic in museums? Are TripAdvisor reviews democratizing cultural authority? That’s a juicy debate!  

5. Own the Blind Spots  
 Sampling bias: Admit that TripAdvisor users ≠ all visitors. Maybe families with toddlers or older folks aren’t posting reviews—how might that skew your data?  
 Lost in translation?: Sentiment in Spanish vs. German might “feel” different. Did cultural nuances (e.g., politeness norms) muddy the waters? Keep it real!  

6. Style Tweaks: Clean Up the Little Things  
 Polish the grammar gems: Fix phrases like “less than 1/3 of the number of parameters than…” → “has fewer than onethird the parameters of…” (Grammar police won’t knock!)  
 Tidy up those references: Missing initials? Inconsistent journal names? A quick sweep here adds polish.  
 Spice up section headers: “Experimental Results” → “What Worked (and What Didn’t)”—grab attention!  

Author Response

Please, see the attachment. All changes were made performed by using Track Changes. 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

No more comments. The manuscript can be published in its present form.

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for your efforts! No further comments from my side