- Presented by slchun
Sentiment analysis has been widely explored in many text domains,
including product reviews, movie reviews, tweets, and so on. However,
there are very fewstudies 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.