Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge

Wenhao Ying, Rong Xiang, Qin Lu

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


Deep learning based general language models have achieved state-of-the-art results in many popular tasks such as sentiment analysis and QA tasks. Text in domains like social media has its own salient characteristics. Domain knowledge should be helpful in domain relevant tasks. In this work, we devise a simple method to obtain domain knowledge and further propose a method to integrate domain knowledge with general knowledge based on deep language models to improve performance of emotion classification. Experiments on Twitter data show that even though a deep language model fine-tuned by a target domain data has attained comparable results to that of previous state-of-the-art models, this fine-tuned model can still benefit from our extracted domain knowledge to obtain more improvement. This highlights the importance of making use of domain knowledge in domain-specific applications.
Original languageEnglish
Title of host publicationProceedings of the 2019 EMNLP Workshop W-NUT: The 5th Workshop on Noisy User-generated Text
Place of PublicationHong Kong
PublisherAssociation for Computational Linguistics (ACL)
Number of pages6
ISBN (Print)978-1-950737-84-0
Publication statusPublished - Nov 2019
EventThe 5th Workshop on Noisy User-Generated Text (W-NUT) of EMNLP 2019 - , Hong Kong
Duration: 4 Nov 20194 Nov 2019


WorkshopThe 5th Workshop on Noisy User-Generated Text (W-NUT) of EMNLP 2019
Country/TerritoryHong Kong


  • multi-label classification
  • Domain knowledge
  • emotion analysis

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