Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains. (C18-1)
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| Challenge: | Standard word embedding algorithms learn vector representations from large corpora of text documents in unsupervised fashion. |
| Approach: | They propose an algorithm that learns word embeddings jointly with a classifier . their algorithm leverages document label information to learn vector representations of words . |
| Outcome: | The proposed algorithm has superior performance on domains with limited data compared to other methods. |
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| Challenge: | Existing word embeddings for sentiment analysis are limited in domain specific applications . generic word embeds are poor initialization for tasks on domain specific data sets. |
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| Challenge: | Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words. |
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| Challenge: | Existing domain adaptation methods for sentiment analysis are sensitive to domain differences, resulting in classifiers that perform poorly on new domains. |
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Domain-Specific Sentiment Lexicons Induced from Labeled Documents (2020.coling-main)
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Joint Embedding of Words and Labels for Text Classification (P18-1)
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Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
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