Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation
نویسندگان
چکیده
Traditional sentiment classification methods often require polarity dictionaries or crafted features to utilize machine learning. However, those approaches incur high costs in the making of dictionaries and/or features, which hinder generalization of tasks. Examples of these approaches include an approach that uses a polarity dictionary that cannot handle unknown or newly invented words and another approach that uses a complex model with 13 types of feature templates. We propose a novel high performance sentiment classification method with stacked denoising auto-encoders that uses distributed word representation instead of building dictionaries or utilizing engineering features. The results of experiments conducted indicate that our model achieves state-of-the-art performance in Japanese sentiment classification tasks.
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