Happy, Nervous or Surprised? Classification of Human Affective States in Social Media


  • Munmun De Choudhury Microsoft Research, Redmond
  • Michael Gamon Microsoft Research, Redmond
  • Scott Counts Microsoft Research, Redmond




social media, twitter, mood, affect, sentiment classification


Sentiment classification has been a well-investigated research area in the computational linguistics community. However, most of the research is primarily focused on detecting simply the polarity in text, often needing extensive manual labeling of ground truth. Additionally, little attention has been directed towards a finer analysis of human moods and affective states. Motivated by research in psychology, we propose and develop a classifier of several human affective states in social media. Starting with about 200 moods, we utilize mechanical turk studies to derive naturalistic signals from posts shared on Twitter about a variety of affects of individuals. This dataset is then deployed in an affect classification task with promising results. Our findings indicate that different types of affect involve different emotional content and usage styles; hence the performance of the classifier on various affects can differ considerably.




How to Cite

De Choudhury, M., Gamon, M., & Counts, S. (2021). Happy, Nervous or Surprised? Classification of Human Affective States in Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 6(1), 435-438. https://doi.org/10.1609/icwsm.v6i1.14335