Sentiment Analysis of National Tourism Organizations on Social Media
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50016698" target="_blank" >RIV/62690094:18450/20:50016698 - isvavai.cz</a>
Result on the web
<a href="http://eldok.svkhk.cz/out/Hradec_Economic_Days/Hradec_Economic_Days_2020/Hradec_Economic_Days_2020.pdf" target="_blank" >http://eldok.svkhk.cz/out/Hradec_Economic_Days/Hradec_Economic_Days_2020/Hradec_Economic_Days_2020.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Sentiment Analysis of National Tourism Organizations on Social Media
Original language description
Social media is probably currently the largest source of human-generated text content. User opinions, feedback, comments, and criticism points to their mood and sentiment towards different topics, especially destinations, products or services. The rapid rise in amount of data and constantly generated content require the need to automate both data acquisition and processing to identify important information and knowledge. Sentiment analysis provides the opportunity to detect opinion, feeling and sentiment from unstructured texts on social media. To analyze the sentiment Machine Learning with Google Natural Language API Client Libraries and Google Cloud SDK (Software development kit) was used. NTOs (National Tourism Organizations) social media have been chosen for analysis in which emotional messages can be expected to stimulate potential visitors to the destination. It was found that all selected NTOs add mostly positive posts and in the sample of two hundred contributions there are only seven with negative polarity of sentiment. There was a moderate correlation between customer growth and positive polarity in the contribution. The results show that creating stable positive descriptions for posts can be one of the key variables for the growth of the fan base and stimulation of potential visitors.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Hradec Economic Days
ISBN
978-80-7435-776-3
ISSN
2464-6059
e-ISSN
2464-6067
Number of pages
7
Pages from-to
250-256
Publisher name
Univerzita Hradec Králové
Place of publication
Hradec Králové
Event location
Hradec Králové
Event date
Apr 2, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000568108700027