Build emotion lexicon from the mood of crowd via topic-assisted joint non-negative matrix factorization

Kaisong Song, Wei Gao, Ling Chen, Shi Feng, Daling Wang, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

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 obtained from the factorization process. We evaluate our lexicon via emotion classification on both benchmark and built-in-house datasets. Results demonstrate the high-quality of our lexicon.

Original languageEnglish
Title of host publicationSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages773-776
Number of pages4
ISBN (Electronic)9781450342902
DOIs
Publication statusPublished - 7 Jul 2016
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: 17 Jul 201621 Jul 2016

Other

Other39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
CountryItaly
CityPisa
Period17/7/1621/7/16

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Keywords

  • Emotion classification
  • Emotion lexicon
  • Joint NMF

ASJC Scopus subject areas

  • Information Systems
  • Software

Cite this

Song, K., Gao, W., Chen, L., Feng, S., Wang, D., & Zhang, C. (2016). Build emotion lexicon from the mood of crowd via topic-assisted joint non-negative matrix factorization. In SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 773-776). Association for Computing Machinery, Inc. https://doi.org/10.1145/2911451.2914759