Learning Sentence Representation for Emotion Classification on Microblogs

1. Learning Sentence Representation for Emotion Classification on Microblogs Duyu Tang, Bing Qin, Ting Liu, Zhenghua Li HIT-SCIR 10/21/2014

2. Outline • Motivation • Representation Learning for Emotion Classification • Experiment Results • Conclusion and Future Work

3. Emotion Analysis on Microblog • Microblog, such as Weibo, has become a popular platform to share opinions about products or hot events.

4. Task • Emotion Classification • Classify a text as Happy, Sad, Angry or Surprise • Prior Work • Supervised [Pang2002, Mishne2006] • Distant Supervision [Go2009, Liu2012] • Unsupervised [Turney2002, Ding2008]

5. Outline • Motivation • Representation Learning for Emotion Classification • Experiment Results • Conclusion and Future Work

6. Representation Learning • Traditional • The bag-of-word representation can not capture the complex linguistic phenomena. • Manufacturing feature engines is time-consuming. • Learning based method • Deep Belief Networks • Text with emoticons

7. Deep Belief Networks • Architecture

8. Data Collection • Emotions from Weibo

9. Input Layer • Top frequent unigrams • Punctuation • Lexicons • Emotion word • Onomatopoeia word • Function word

10. Outline • Motivation • Representation Learning for Emotion Classification • Experiment Results • Conclusion and Future Work

11. Dataset • Emoticon data • 20,000 weibo (balanced for each category) • Labeled data

12. Results

13. Results

14. Outline • Motivation • Representation Learning for Emotion Classification • Experiment Results • Conclusion and Future Work

15. Conclusion • Emotion classification on Weibo • Learning sentence representation on emoticon data via deep belief network • For future • Word representation for sentiment analysis • Compositionality for sentiment analysis

16. Questions?

17. Back Up

18. Emoticon Selection

19. Detailed Params • Lexicons • Architecture