Papers with multi-domain sentiment analysis

2 papers
Learning Domain Representation for Multi-Domain Sentiment Classification (N18-1)

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Challenge: Training data for sentiment analysis is abundant in multiple domains, yet scarce for other domains.
Approach: They propose to use domain-specific representations of input sentences to improve sentiment classification . they use a descriptor vector to map adversarially trained domain-general Bi-LSTM inputs into domain- specific representations .
Outcome: The proposed model outperforms existing methods on multi-domain sentiment analysis significantly.
What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training (N18-2)

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Challenge: a key roadblock is application to new domains, unseen in training.
Approach: They propose a method to optimise in- and out-of-domain accuracy by combing domain-specific and domain-general components with adversarial training for domain.
Outcome: The proposed method improves on domain adaptation and domain-adversarial training.

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