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Qualifying Exam
12/14/2018 11:30 am
Hill 482

AffectVec: Word representation for fine-grain emotion analysis

Shahab Raji, Dept. of Computer Science

Examination Committee: Prof. Gerard de Melo (Chair), Prof. Matthew Stone, Prof. Yongfeng Zhang and Prof. Srinivas Narayana


Emotion classification plays a key role in understanding human communications in the modern era of social media. While previous research have focused mainly on eight or fewer emotions, in this work we introduce AffectVec, an embedding incorporating 68 emotions. Our approach improves previous systems in a number of ways; first, the fine-grained vectors better represent the richness of human motions, second, unlike common vector representation techniques the scores on each dimension are interpretable. We compare our embedding to human-annotated corpora and vector representations in a series of statistical and machine learning experiments.