Detecting COVID-19 Pandemic Using Sentiment Analysis of Tweets
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A90101%2F21%3A10441826" target="_blank" >RIV/00216208:90101/21:10441826 - isvavai.cz</a>
Výsledek na webu
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=9VIebPvRcs" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=9VIebPvRcs</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Detecting COVID-19 Pandemic Using Sentiment Analysis of Tweets
Popis výsledku v původním jazyce
From 2019, the world is facing an unforeseen challenge in the form of COVID-19, which started in Wuhan (China), and within two months, it spread to 212 countries. The coronavirus disease (COVID-19) pandemic puts unprecedented pressure on healthcare systems worldwide. Due to its rapid widespread around the globe affecting the lives of millions, extensive measures to reduce and prevent its transmission have been implemented. One of which is to shut down their cities completely. During this Pandemic, people started to express their situations through social media tools. In natural language processing, valuable insights can be captured from textual data taken from different social media platforms. In this research work, data related to COVID-19 is collected from a popular social networking site, Twitter. The tweets gathered are refined through pre-processing for text mining and sentiment analysis. From this data, we successfully detect the actual count of people who may be affected by the COVID-19 Pandemic using sentimental analysis and machine learning techniques.
Název v anglickém jazyce
Detecting COVID-19 Pandemic Using Sentiment Analysis of Tweets
Popis výsledku anglicky
From 2019, the world is facing an unforeseen challenge in the form of COVID-19, which started in Wuhan (China), and within two months, it spread to 212 countries. The coronavirus disease (COVID-19) pandemic puts unprecedented pressure on healthcare systems worldwide. Due to its rapid widespread around the globe affecting the lives of millions, extensive measures to reduce and prevent its transmission have been implemented. One of which is to shut down their cities completely. During this Pandemic, people started to express their situations through social media tools. In natural language processing, valuable insights can be captured from textual data taken from different social media platforms. In this research work, data related to COVID-19 is collected from a popular social networking site, Twitter. The tweets gathered are refined through pre-processing for text mining and sentiment analysis. From this data, we successfully detect the actual count of people who may be affected by the COVID-19 Pandemic using sentimental analysis and machine learning techniques.
Klasifikace
Druh
J<sub>ost</sub> - Ostatní články v recenzovaných periodicích
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
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Návaznosti
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Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Artificial Intelligence Theory and Applications
ISSN
2757-9778
e-ISSN
2757-9778
Svazek periodika
1
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
TR - Turecká republika
Počet stran výsledku
9
Strana od-do
39-47
Kód UT WoS článku
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EID výsledku v databázi Scopus
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