A regression approach to valence-Arousal ratings of words from word embedding

Minglei Li, Yunfei Long, Qin Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Traditional affective lexicons are mainly based on discrete classes, such as positive, happiness, sadness, which may limit its expressive power compared to the dimensional representation in which affective meanings are expressed through continuous numerical values on multiple dimensions, such as valence-Arousal. Traditional methods for acquiring dimensional lexicons are mainly based on time-consuming manual annotation. In this paper, we propose a regression-based method to automatically infer the valence-Arousal ratings of words via their word embedding. This method is based on the assumption that different features in word embedding contribute differently to different affective meanings. Experiments on three valence-Arousal lexicons show that our method outperforms the state-of-The-Art method on all the lexicons under four different evaluation metrics. Our model also has superior computation advantage over the state-of-The-Art model.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Asian Language Processing, IALP 2016
Number of pages4
ISBN (Electronic)9781509009213
Publication statusPublished - 10 Mar 2017
Event20th International Conference on Asian Language Processing, IALP 2016 - Tainan, Taiwan
Duration: 21 Nov 201623 Nov 2016


Conference20th International Conference on Asian Language Processing, IALP 2016


  • Arousal
  • Regression
  • Valence
  • Word Embedding

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Linguistics and Language
  • Artificial Intelligence

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