Analyzing public opinions regarding virtual tourism in the context of COVID-19: Unidirectional vs. 360-degree videos

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/70883521:28140/23:63565098

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Analyzing public opinions regarding virtual tourism in the context of COVID-19: Unidirectional vs. 360-degree videos

Popis výsledku v původním jazyce

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&apos; 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.

Název v anglickém jazyce

Analyzing public opinions regarding virtual tourism in the context of COVID-19: Unidirectional vs. 360-degree videos

Popis výsledku anglicky

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&apos; 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF19_073%2F0016941" target="_blank" >EF19_073/0016941: Juniorské granty UTB ve Zlíně</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Information

ISSN

2078-2489

e-ISSN

2078-2489

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

000917600000001

EID výsledku v databázi Scopus

2-s2.0-85146754375