Research Article
Hybrid Deep Learning Based Model on Sentiment Analysis of Peer Reviews on Scientific Papers
@INPROCEEDINGS{10.1007/978-3-031-35081-8_9, author={Ritika Sarkar and Prakriti Singh and Mustafa Musa Jaber and Shreya Nandan and Shruti Mishra and Sandeep Kumar Satapathy and Chinmaya Ranjan Pattnaik}, title={Hybrid Deep Learning Based Model on Sentiment Analysis of Peer Reviews on Scientific Papers}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Peer reviews Sentiment analysis Natural Language Processing AI algorithms}, doi={10.1007/978-3-031-35081-8_9} }- Ritika Sarkar
Prakriti Singh
Mustafa Musa Jaber
Shreya Nandan
Shruti Mishra
Sandeep Kumar Satapathy
Chinmaya Ranjan Pattnaik
Year: 2023
Hybrid Deep Learning Based Model on Sentiment Analysis of Peer Reviews on Scientific Papers
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_9
Abstract
The peer review process involved in evaluating academic papers submitted to journals and conferences is very perplexing as at times the scores given by the reviewer may be poor in contrast with the textual comments which are in a positive light. In such a case, it becomes difficult for the judging chair to come to a concrete decision regarding the accept or reject decision of the papers. In our paper, we aim to extract the sentiment from the reviewers’ opinions and use it along with the numerical scores to correlate that in order to predict the orientation of the review, i.e., the degree of acceptance. Our proposed methods include Machine learning models like Naive Bayes, Deep learning models involving LSTM and a Hybrid model with BiLSTM, LSTM, CNN, and finally Graph based model GCN. The dataset is taken from the UCI repository consisting of peer reviews in Spanish along with other parameters used for judging a paper. Bernoulli’s Naive Bayes was the model that fared the highest amongst all the approaches, with an accuracy of 75.61% after varying the parameters to enhance the accuracy.
