(ICCC 2022) MCPR: A Chinese Product Review Dataset for Multimodal Aspect-Based Sentiment Analysis

Carol Xu (Vanke Meisha Academy, China); Xuan Luo (Harbin Institute of Technology, Shenzhen, China); Dan Wang
(Shenzhen Zhenli Technology Co. Ltd., China)
Aspect-based sentiment analysis (ABSA), which aims to analyze the sentiments toward the extracted aspects, has been
attracting considerable interest in the last decade. Most of the existing studies concentrate on determining the sentiment
polarity of the given aspect according to only textual content, while there is little research on multimodal aspect-based
sentiment analysis (MABSA) due to the scarcity of datasets consisting of multimodality content, such as both texts and
images. In this paper, we design and construct a Multimodal Chinese Product Review dataset (MCPR) to support the
research of MABSA. MCPR is a collection of 1.5k product reviews involving clothing and furniture departments, from the
e-commercial platform JD.com. After aspect-base sentiment annotation and text-image matching, we obtain 2,719 text�image pairs and 610 distinct aspects in total. It is the first aspect-based multimodal Chinese product review dataset.