Naive Bayes vs Svm vs Ruled
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Transcript of Naive Bayes vs Svm vs Ruled
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Tweet Classifcation
Mentor: Romil Bansal
GROUP NO-37 Manish
Jindal(201305578) TrilokSharma(201206527)
Guided by : Dr. Vasudeva Vara
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Proble !tateent : To automatically
classify Tweets from Twitter into various genresbased on predened !ikipedia "ategories#
"otivation:o Twitter is a ma$or social networking service with
over %&& million tweets made every day# o Twitter provides a list of Trending Topics in real
time' but it is often hard to understand what thesetrending topics are about#
o (t is important and necessary to classify thesetopics into general categories with high accuracyfor better information retrieval#
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)ata
)ataset :o (nput )ata is the static * real+time data consisting of
the user tweets#
o Training dataset :
,etched from twitter with twitter-$ api#
,inal )eliverable:o (t will return list of all cate#ories to which the input
tweet belongs#
o (t will also give the accuracy o$ t%e al#orit% usedfor classifying tweets#
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"ategories!e took following categories into considerationfor classifying twitter data#
./Business 0/1aw 2/3olitics
%/4ducation 5/1ifestyle .&/Sports
6/4ntertainment 7/8ature../Technology
-/9ealth /3laces
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"oncepts used for better performance
;utliers removal To remove low fre
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;ther "oncepts used ##Spelling "orrection To correct spellings using 4dit distance method#
8amed 4ntity Recognition:,or ranking result category and nding most
appropriate#
Synonym form
(f feature?word/ of test
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Tweets "lassication @lgorithms!e used 6 algorithms for classication
./ 8aAve based
%/ SM basedSupervised6/ Rule based
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"rawltweeter
data
Tweets"leaning'Stop wordremoval
"reate (ndeCle
;f featurevector
4Ctract ,eatures
?Dni
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Main idea for Supervised 1earning&ssu'tion: training set consists of
instances of diFerent classes described cj ascon$unctions of attributes values
Tas(: "lassify a new instance d based on atuple of attribute values into one of theclasses cj ∈ C
)ey idea: assign the most probable classusing supervised learning algorithm#
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Method . : Bayes "lassier
Bayes rule states :
!e used G!4=@H library for machine learning inBayes "lassier for our pro$ect#
8ormaliIation"onstant
1ikelihood 3rior
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Method % : SM "lassier
?Support ector Machine/iven a new point *' we can score its
pro$ection onto the hyperplane normal:
(#e#' compute score: wT* K b L αi y i*iT* +b)ecide class based on whether N or O &
"an set condence threshold t #
..+.
&.
Score O t :yes
Score N +t :
no
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.%
Multi+class SM
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.6
Multi+class SM @pproaches,-a#ainst-all
4ach of the SMs separates a single class fromall remaining classes ?"ortes and apnik' .220/
1-against-1
3air+wise# k (k -1)/2, k ∈ Y SMs are trained# 4achSM separates a pair of classes ?,ridman' .225/
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@dvantages of SM9igh dimensional input space
,ew irrelevant features ?dense concept/
Sparse document vectors ?sparse instances/
TeCt categoriIation problems are linearlyseparable
,or linearly inseparable data we can use (ernels to map data into high dimensional space' so thatit becomes linearly separable with hyperplane#
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Method 6 : Rule Based
!e dened set of rule to classify a tweetbased on term fre
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4Cample+ TweetLsachin is a good player' who eats apple
and banana which is good for health#
,eature+ sachin'player'eats'apple'health'banana
Stop word+is'a'good'he'was'for'which'and'who
"lassication+ eature-cate#ory ter-$reuency
sachin-sports 2000
player-sports 900
eating-health 500apple-technology 1000
health-health 800
banana-health 700
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MaC term+fre
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"ross+validation ?@ccuracy/Steps for k+fold cross+validation : Step .: split data into k subsets ofe
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@ccuracy Results ? .& folds/Accuracy of Algorithm in %
Categories\ Algo. S! "a#$e %&le
Business 86'6 81' 98'30
Education 85'71 76'07 81'8
Entertainment 86'8 79'1 87'9Health 95'67 8'62 90'93
Law 81'17 73'38 75'25
Lifestyle 93'27 89'71 82'2
Nature 87'0 78'6 8'2
laces 81'01 75'35 80'73
olitics 81'91 81'88 76'31
!"orts 87'11 83'57 81'87
#echnology 83'6 82' 77'05
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Dni
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Snapshot
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Result
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@ccuracy
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Thank You