Analyzing public opinions regarding virtual tourism in the context of COVID-19: Unidirectional vs. 360-degree videos
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F23%3A63565098" target="_blank" >RIV/70883521:28120/23:63565098 - isvavai.cz</a>
Alternative codes found
RIV/70883521:28140/23:63565098
Result on the web
<a href="https://www.mdpi.com/2078-2489/14/1/11" target="_blank" >https://www.mdpi.com/2078-2489/14/1/11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/info14010011" target="_blank" >10.3390/info14010011</a>
Alternative languages
Result language
angličtina
Original language name
Analyzing public opinions regarding virtual tourism in the context of COVID-19: Unidirectional vs. 360-degree videos
Original language description
Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidirectional videos is enormous and might differ between the two kinds of videos. This presents the problem of efficiently evaluating user opinions with respect to which type of video will be more appealing to viewers, positive comments, or interest. This paper aims to study SentiStrength-SE and SenticNet7 techniques for sentiment analysis. The findings demonstrate that the sentiment analysis obtained from SenticNet7 outperforms that from SentiStrength-SE. It is revealed through the sentiment analysis that sentiment disparity among the viewers of 360-degree and unidirectional videos is low and insignificant. Furthermore, the study shows that unidirectional videos garnered the most traffic during COVID-19 induced global travel bans. The study elaborates on the capacity of unidirectional videos on travel and the implications for industry and academia. The second aim of this paper also employs a Convolutional Neural Network and Random Forest for sentiment analysis of YouTube viewers' comments, where the sentiment analysis output by SenticNet7 is used as actual values. Cross-validation with 10-folds is employed in the proposed models. The findings demonstrate that the max-voting technique outperforms compared with an individual fold.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF19_073%2F0016941" target="_blank" >EF19_073/0016941: Junior grants of TBU in Zlin</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information
ISSN
2078-2489
e-ISSN
2078-2489
Volume of the periodical
14
Issue of the periodical within the volume
1
Country of publishing house
CH - SWITZERLAND
Number of pages
18
Pages from-to
1-18
UT code for WoS article
000917600000001
EID of the result in the Scopus database
2-s2.0-85146754375