Lenin Ravindranath Sivalingam 25 million users around the world Over 10 million tweets a day People often express opinions Richest source of user opinions Extracting movie reviews 3000 tweets tagged by 40 users ID: 595269
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Opinion Mining From Twitter Feeds
Lenin Ravindranath Sivalingam
25 million users around the world
Over 10 million tweets a day
People often express opinions!
Richest source
of user opinions!
Extracting movie
reviews!
3000 tweets tagged by 40 users
Sentiment Analysis on Tweets
Data Collection
Crowd Sourcing
Positive review
Just watched 2012, it was
pretty good...
Negative review
Saw 2012…… worst movie
ever….. waste of time
Mixed review
2012 – great effects, worst
acting, was ok.
Not a review
Went to see 2012 with my
girlfriend
Tweet Extraction
Twitter Keyword Search
Tweet Cleaning
Internet Slang Translation
Phrase Extraction
Spelling Correction
POS Tagging
SVM Classifier
Feature Extraction
Polar Words Extraction
(Polar Features)
Polarity Analysis
(Polarity Reversal)
Feature Encoding
(Phrase polarity, position)
Just saw 2012 – the movie was good, but not gr8… CG was awesum…
Lexicon
Tweet
Cleaned phrases
Tagged phrases
Review
Features
just saw MMM
the movie was good
but not great
CG was awesome
Just
/RB
saw
/VB
MMM
/NN
The
/DT
movie
/NN
was
/VB good/JJBut/CC not/RB great/JJ CG/NN was/VB awesome/JJ
MMMgood/JJnot/RB great/JJawesome/JJ
MMMPOS-JJNEG-JJPOS-JJ
MMMPOS-JJNEG-RB POS-JJPOS-JJ
1:0 2:0 3:1 4:0 5:0 6:1 7:1 8:0
Mixed review
Internet slang lexicon
Results
Polarized Lexicon
# Positive words
# Negative wordsAdjective7467Verb2926Nouns2930Adverbs2611
Polarized lexicon
Couples Retreat was hilarious.
2012 was hilarious.
New moon was ahhwwesum..
I saw 2012 today with my girlfriend. I love her.
I just saw 2012. I need my money back.
Ambiguous polar words
Too much slang
No polar words
Different context
Why errors?
Approaches
Correct predictionBag of words39.24%Adjectives44.27%Polar words58.34%Our approach (Negation + Polarity + Position)76.53%
ClassesCorrect predictionPositive review tweets80.84%Negative review tweets69.03%Mixed review tweets57.67%No review tweets80.78%Classifying subjective/objective tweets83.50%
Number of phrases to include as features
1:0 2:0
3:1 4:0
5:0 6:1
7:1 8:0