covid

<?php eelementsin“cricket. //CreateanewDOMdocument $doc=newDOMDocument(); //Createtherootelement $cricketTeam=$doc->createEl ement(“Cri //Createthefirstteamel cketTeam”); ementforAustrali a $teamAustrali a=$doc->createElement(“Team”); $teamAustrali a->setAttri bute(“country”,“Austral //Createtheplayerel ementandsetitsvalue $player1=$doc->createEl ement(“player”,“SteveSmi $teamAustrali a->appendChil d($player1); eelementsasshownbelow: xml”fil eofcategory,country=”I ndia”. ia”); th”); //Createtherunselementandsetitsvalue $runs1=$doc->createEl ement(“runs”,“7090”); $teamAustrali a->appendChil d($runs1); //Createthewicketelementandsetitsval $wicket1=$doc->createEl ue ement(“wicket”,“17”); $teamAustrali a->appendChil d($wicket1); //Appendtheteamelementtotherootelement $cricketTeam->appendChi ld($teamAustral ia); //CreatethesecondteamelementforIndia $teamIndia=$doc->createEl ement(“Team”); $teamIndia->setAttri bute(“country”,“I ndia”); //Createtheplayerel ementandsetitsvalue $player2=$doc->createEl ement(“player”,“Vi $teamIndia->appendChi ld($pl ayer2); //Createtherunselementandsetitsvalue $runs2=$doc->createEl ratKohli”); ement(“runs”,“12169”); $teamIndia->appendChi ld($runs2); //Createthewicketelementandsetitsval $wicket2=$doc->createEl ue ement(“wicket”,“4”); $teamIndia->appendChi ld($wicket2); //Createthecategoryel ementandsetitsvalue $category=$doc->createEl ement(“category”,“Captai n”); $teamIndia->appendChi ld($category); //Appendtheteamelementtotherootelement $cricketTeam->appendChi ld($teamIndi a); //Appendtherootelementtothedocument $doc->appendChil d($cricketTeam); //SavetheXMLfile $doc->save(“cri cket.xml”); Echo“Elementsaddedsuccessfull ?>

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Importpandasaspd
ni/youtube
csv
onsonit.
mentanalysisandfindthepercentageof
Importnltk
Fromnltk.senti
ment.vaderimportSenti
mentIntensi
#readthedataset
Df=pd.read_csv(‘
covid_2021_1.csv’
)
#removenullvaluesandduplicates
Df.dropna(i
nplace=True)
Df.drop_dupl
icates(subset=’
tyAnalyzer
Comment’,inplace=True)
#tokenizecommentsinwords
Nltk.downl
oad(‘punkt’
)
Df[‘
tokens’
]=df[‘Comment’
].appl
#performsentimentanalysis
Nltk.downl
oad(‘vader_l
exicon’
Sia=SentimentIntensi
)
y(nltk.
word_tokenize)
tyAnalyzer()
Df[‘
sentiment’
]=df[‘Comment’
].appl
y(lambdax:sia.pol
#calculatepercentageofposi
tive,negati
Total_comments=len(df)
Positi
ve_comments=len(df[df[‘
sentiment’
Negative_comments=len(df[df[‘
sentiment’
Neutral_comments=len(df[df[
‘senti
Positi
ve_percentage=(posi
arity_scores(x)[
ve,andneutralcomments
]>0])
]<0])
ment’]==0])
tive_comments/total_comments)*100
Negative_percentage=(negati
‘compound’])
ve_comments/total_comments)*100
Neutral_percentage=(neutral
_comments/total_comments)*100
#printtheresults
Print(‘
TotalComments:’
,total
_comments)
Print(‘
Positi
veComments:’,positi
Print(‘
NegativeComments:’
ve_comments,‘(‘
,positi
ve_percentage,‘
,negative_comments,‘
(‘,negati
%)’)
ve_percentage,‘
%)’)
Print(‘
NeutralComments:’
,neutral_percentage,'%)')