Keyboard worriers
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Tag | 1 | 2 | Valor |
---|---|---|---|
LDR | 00000cab a2200000 4500 | ||
001 | MAP20210006654 | ||
003 | MAP | ||
005 | 20220911211039.0 | ||
008 | 210225e20210201gbr|||p |0|||b|eng d | ||
040 | $aMAP$bspa$dMAP | ||
084 | $a219 | ||
100 | $0MAPA20210003936$aNg, John | ||
245 | 1 | 0 | $aKeyboard worriers$cJohn Ng, Melanie Zhang |
520 | $aTwitter offers the public the opportunity to give their real-time thoughts on world events by posting short texts known as tweets'. The COVID-19 pandemic was the defining event of 2020, which makes it a great subject for sentiment analysis the use of natural language processing to automatically determine the emotion a writer is expressing in a piece of text or tweets. We wanted to see if we could use Twitter data relating to COVID-19 in the UK to uncover insights pertinent to public interest and the insurance industry. | ||
650 | 4 | $0MAPA20080578848$aAnálisis de datos | |
650 | 4 | $0MAPA20080624019$aComportamiento del consumidor | |
650 | 4 | $0MAPA20200005599$aCOVID-19 | |
650 | 4 | $0MAPA20080552022$aPandemias | |
650 | 4 | $0MAPA20080586294$aMercado de seguros | |
700 | 1 | $0MAPA20210003943$aZhang, Melanie | |
773 | 0 | $wMAP20200013259$tThe Actuary : the magazine of the Institute & Faculty of Actuaries$dLondon : Redactive Publishing, 2019-$g01/02/2021 Número 1 - febrero 2021 , p. 27-29 |