Machine learningDeep learning / NLP / CV

Domain-adaptive Sentiment Analysis

Domain-adaptive sentiment analysis trains a sentiment model on one or more labeled source domains (e.g., product reviews) and adapts it to a target domain (e.g., social media posts or news) where labels are scarce or absent. By bridging the vocabulary and distributional gap between domains, it achieves strong sentiment classification without requiring large labeled corpora in every target domain.

MethodMind'de açSoonVideoSoon

Tam yöntemi oku

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), 440–447. link
  2. Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. Proceedings of the 19th International Conference on World Wide Web (WWW), 751–760. DOI: 10.1145/1772690.1772767

Related methods

ScholarGateDomain-adaptive Sentiment Analysis (Domain-adaptive Sentiment Analysis (Cross-Domain Opinion Mining with Domain Adaptation)). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/domain-adaptive-sentiment-analysis