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Unsupervised Graph-Based Patterns Extraction for Emotion
Classification
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● Kim et al. (2009)○ Detecting sadness in 140 characters.
●Jansen et al. (2009)○ Twitter power: Tweets as electronic word of mouth.
●Pandey et al. (2010)○ Sentiment analysis of microblogs.
● Volvoka et al. (2013)○ Exploring sentiment in social media: Bootstrapping subjectivity clues from
multilingual twitter streams
Only focused on positive/negative polarity classification
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A multilingual system should incorporate new languages easily
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Locked in at ASONAM Conference #happy
ASONAM 2015 Conference will be hosted in Paris this year
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How to obtain them in a way that is independent of the language?
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• emotion concepts will tend to be clustered together (as in many emotion words interconnected by the same connector words)
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Anticipation:
p1 p2pn...
Joy:
p1 p2pn...
Trust:
p1 p2pn...
How do we calculate which patterns are more relavant to each emotion?
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