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.

本文提出了一个基于Youtube产品评论视频的首个数据集，以及一个新的关注RNN模型，用于方面提取和联合方面提取和情感分类，同时，该模型在SemEvalABSA语料库上提供了最先进的方面提取性能。

使用注意力RNN挖掘YouTube视频的闭幕字幕上的细粒度意见