Transfer learning across two sentiment classes using deep learning
用深度学习的跨情感分类的迁移学习
情感分析主要用于预测人们在自然语言中表达的思想和情感。
摘要部分:two types of sentiment:sentiment polarity and politeness。
语义极性和politeness (礼貌用语)
数据集:在线资源数据库
训练源领域和目标领域的混合数据导致性能极大的提升。
Transfer learning methods could be very useful in performing predictiond inpecialized tasks using
models trained on a related general task. This coule help sentiment analysisi models to generalize
well to areas with very little annotated data available.
目前在情感分析方面已经做的工作:使用标记数据来训练有监督学习算法。
问题所在:或者这些数据是困难的、昂贵的、费时的等等,问题由此出现:极性语料库是否可以作为通用源用于学习和预测不同种类的情感例如生气、高兴、有礼貌。
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