Build Emotion Lexicon from the Mood of Crowd via Topic-Assisted Joint Non-negative Matrix Factorization

@article{Song2016BuildEL,
  title={Build Emotion Lexicon from the Mood of Crowd via Topic-Assisted Joint Non-negative Matrix Factorization},
  author={Kaisong Song and Wei Gao and Ling Chen and Shi Feng and Daling Wang and Chengqi Zhang},
  journal={Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval},
  year={2016}
}
  • Kaisong Song, Wei Gao, Chengqi Zhang
  • Published 7 July 2016
  • Computer Science
  • Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
In the research of building emotion lexicons, we witness the exploitation of crowd-sourced affective annotation given by readers of online news articles. Such approach ignores the relationship between topics and emotion expressions which are often closely correlated. We build an emotion lexicon by developing a novel joint non-negative matrix factorization model which not only incorporates crowd-annotated emotion labels of articles but also generates the lexicon using the topic-specific matrices… 

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