8/17/2019 Naive Bayes vs Svm vs Ruled

1/24

Tweet Classifcation

Mentor: Romil Bansal

GROUP NO-37 Manish

 Jindal(201305578) TrilokSharma(201206527)

Guided by : Dr. Vasudeva Vara

8/17/2019 Naive Bayes vs Svm vs Ruled

2/24

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#

8/17/2019 Naive Bayes vs Svm vs Ruled

3/24

)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#

8/17/2019 Naive Bayes vs Svm vs Ruled

4/24

"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

8/17/2019 Naive Bayes vs Svm vs Ruled

5/24

"oncepts used for better performance

;utliers removal To remove low fre

8/17/2019 Naive Bayes vs Svm vs Ruled

6/24

;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

8/17/2019 Naive Bayes vs Svm vs Ruled

7/24

 Tweets "lassication @lgorithms!e used 6 algorithms for classication

./ 8aAve based

%/ SM basedSupervised6/ Rule based

8/17/2019 Naive Bayes vs Svm vs Ruled

8/24

"rawltweeter

data

 Tweets"leaning'Stop wordremoval

"reate (ndeCle

;f featurevector

4Ctract ,eatures

?Dni

8/17/2019 Naive Bayes vs Svm vs Ruled

9/24

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#

8/17/2019 Naive Bayes vs Svm vs Ruled

10/24

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

8/17/2019 Naive Bayes vs Svm vs Ruled

11/24

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

8/17/2019 Naive Bayes vs Svm vs Ruled

12/24

.%

Multi+class SM

 

8/17/2019 Naive Bayes vs Svm vs Ruled

13/24

.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/

8/17/2019 Naive Bayes vs Svm vs Ruled

14/24

@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#

8/17/2019 Naive Bayes vs Svm vs Ruled

15/24

Method 6 : Rule Based

!e dened set of rule to classify a tweetbased on term fre

8/17/2019 Naive Bayes vs Svm vs Ruled

16/24

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

8/17/2019 Naive Bayes vs Svm vs Ruled

17/24

MaC term+fre

8/17/2019 Naive Bayes vs Svm vs Ruled

18/24

"ross+validation ?@ccuracy/Steps for k+fold cross+validation :  Step .: split data into k subsets ofe

8/17/2019 Naive Bayes vs Svm vs Ruled

19/24

@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

8/17/2019 Naive Bayes vs Svm vs Ruled

20/24

Dni

8/17/2019 Naive Bayes vs Svm vs Ruled

21/24

Snapshot

8/17/2019 Naive Bayes vs Svm vs Ruled

22/24

Result

8/17/2019 Naive Bayes vs Svm vs Ruled

23/24

@ccuracy

8/17/2019 Naive Bayes vs Svm vs Ruled

24/24

 Thank You