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Done this week  Getting familiar with corpus (100,000 words) of blog posts with corresponding emotions  NLP feature extraction  Multinomial logistic.

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Presentation on theme: "Done this week  Getting familiar with corpus (100,000 words) of blog posts with corresponding emotions  NLP feature extraction  Multinomial logistic."— Presentation transcript:

1 Done this week  Getting familiar with corpus (100,000 words) of blog posts with corresponding emotions  NLP feature extraction  Multinomial logistic regression for emotion classification  Ran classifier on sentence, returned confidence scores for each emotion

2 Features  Blog post: I'm so happy, nothing can get me down. Features word antici patio n sadne ssjoy disgus ttrustanger surpris efear positiv e negat ive prior_ polarit y prior_ polarit y_stre ngthpos-2pos-1Pos0Pos1pos2neg advm odamod modifi ed_by _positi ve modifi es_po sitive modifi es_ne gativ e modifi ed_by _neg ative happ y1010100010 Positiv e Stron gsubjVBPRBJJ,NN0100000

3 Results  Blog post: I'm so happy, nothing can get me down. angerantic.disgust fear joysadness surprise trust

4 Results  Blog post: I'm not happy, nothing can pick me up. angerantic.disgust fear joysadness surprise trust

5 Results angerantic.disgust fear joysadness surprise trust  Blog post: Well, my mom just told me the most scary news I've ever heard about myself. ?

6 Future Work  Improve emotion classifier results  Improve emotional word detection  Integrate Sentibank’s ANPs  Movie image corpus  Images from movies, corresponding descriptions  Sentibank for labeling emotion  Train  Learning to map descriptive to expressive sentences


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