1. Influence detection of famous personalities using Politeness and Likeability
Navita Jain

2. Data Twitter data 2 different types of data
For likeability or attitude detection: A dataset of tweets in which each influential or non-influential user is referred.
Example tweets :
FOUR Doctors Warn - Trump Has a Narcissistic Personality Disorder! Unstable 2b President? #DonaldsDisorder #VOAV https…
For politeness detection: A dataset of tweets, tweeted by each influential or non- influential user.
Example tweets:
@DanScavino Vote trump to save the west. Don't become like Europe - #WakeUpAmerica

3. Ground Truth Influential users: From Time magazine 1. Barack Obama
2. Donald Trump
3. Narendra Modi
4. Kim Kardashian
5. Taylor Swift
Non-influential users: Partial from CEOWorld.biz and Partial Self
1. Jeb Bush
2. John Boehner
3. Rahul Gandhi
4. Sarah Palin
5. Johnny Depp

4. Data Pre-processing Remove emoticons, links, hashtags
Remove numbers, punctuations, stopwords(a, the, ask, just…)
Convert all the alphabets to lowercase for consistency
Remove statements that do not convey a message

5. Feature Vector Likeable Feature Vector
Since each tweet is referred to a specific test user, detect Positive or negative sentiment.
Politeness Feature Vector
Create Politeness Feature Vector [‘pardon me’, ‘thank you’, ‘if I may say’,’please’, ’shut up’, ’go to hell’,…….].
Annotate data and train classifier

6. Influence Detection Politeness correlates positively with influence
Higher Politeness score more influential a person should be.
Likeability proportional to influence
First iteration, sum the score if above some threshold : influential

7. Thanks Suggestions

8. Twitter Api command for Data collection
''' Tweets on : user ''‘
raw_tweets = myApi.GetSearch('Donald Trump',lang='en',count=100)
‘’' Tweets by user''‘
raw_tweets = ,exclude_replies='true')