期刊
ACM/SIGIR PROCEEDINGS 2018
卷 -, 期 -, 页码 175-184出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3209978.3210056
关键词
Sentiment analysis; peer review mining; multiple instance learning; abstract-based memory mechanism
资金
- National Natural Science Foundation of China [61772036, 61331011]
- Key Laboratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology)
Sentiment analysis has been widely explored in many text domains, including product reviews, movie reviews, tweets, and so on. However, there are very few studies trying to perform sentiment analysis in the domain of peer reviews for scholarly papers, which are usually long and introducing both pros and cons of a paper submission. In this paper, we for the first time investigate the task of automatically predicting the overall recommendation/decision (accept, reject, or sometimes borderline) and further identifying the sentences with positive and negative sentiment polarities from a peer review text written by a reviewer for a paper submission. We propose a multiple instance learning network with a novel abstract-based memory mechanism (MILAM) to address this challenging task. Two evaluation datasets are constructed from the ICLR open reviews and evaluation results verified the efficacy of our proposed model. Our model much outperforms a few existing models in different experimental settings. We also find the generally good consistency between the review texts and the recommended decisions, except for the borderline reviews.
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