Papers with sentiment labeling

2 papers
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text (P19-1)

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Challenge: Existing sentiment detection methods are trained on sentiment-labeled monolingual text.
Approach: They propose a method for synthesizing labeled code-switched text from monolingual text.
Outcome: The proposed method improves sentiment labeling accuracy for three languages.
NLP Service APIs and Models for Efficient Registration of New Clients (2020.findings-emnlp)

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Challenge: State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP.
Approach: They propose a centralized NLP service that can be customized to suit clients . they propose NER, sentiment labeling, and predictive language modeling to improve client experience.
Outcome: The proposed model can be used to improve word usage and salience across clients without re-training or fine-tuning.

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