Abstract
Video reviews are the natural evolution of written product reviews. In this paper we target this phenomenon and introduce the first dataset created from closed captions of YouTube product review videos as well as a new attention-RNN model for aspect extraction and joint aspect extraction and sentiment classification. Our model provides state-of-the-art performance on aspect extraction without requiring the usage of hand-crafted features on the SemEval ABSA corpus, while it outperforms the baseline on the joint task. In our dataset, the attention-RNN model outperforms the baseline for both tasks, but we observe important performance drops for all models in comparison to SemEval. These results, as well as further experiments on domain adaptation for aspect extraction, suggest that differences between speech and written text, which have been discussed extensively in the literature, also extend to the domain of product reviews, where they are relevant for fine-grained opinion mining.
Abstract (translated)
视频评论是书面产品评论的自然演变。在本文中，我们针对这一现象，介绍从YouTube产品评论视频的隐藏字幕创建的第一个数据集，以及用于方面提取和联合方面提取和情感分类的新注意 - RNN模型。我们的模型在方面提取方面提供了最先进的性能，而不需要在SemEval ABSA语料库上使用手工特征，而在联合任务上则优于基线。在我们的数据集中，注意 - RNN模型优于两种任务的基线，但我们观察到与SemEval相比，所有模型的重要性能下降。这些结果以及关于方面提取领域适应性的进一步实验表明，文献中已经广泛讨论的语音和书面文本之间的差异也延伸到产品评论的领域，其中它们与细粒度相关意见挖掘。
URL
https://arxiv.org/abs/1708.02420
