Acceptance Decision Prediction in Peer-Review Through Sentiment Analysis
| Autor: | Henrique Lopes Cardoso, Amanda Sizo, Luís Paulo Reis, Ana Carolina Ribeiro |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Progress in Artificial Intelligence ISBN: 9783030862299 EPIA |
| DOI: | 10.1007/978-3-030-86230-5_60 |
| Popis: | Peer-reviewing is considered the main mechanism for quality control of scientific publications. The editors of journals and conferences assign submitted papers to reviewers, who review them. Therefore, inconsistencies between reviewer recommendations and reviewer comments are a problem that the editor needs to handle. However, few studies have explored whether it is possible to predict the reviewer recommendation from review comments based on NLP techniques. This study aims to predict reviewer recommendation of the scientific papers they review (accept or reject) and predict reviewers’ final scores. We used a dataset composed of 2,313 review texts from two computer science conferences to test our approach, based on seven ML algorithms on regression and classification tasks and VADER application. SVM and MLP Classifier achieved the best performance in the classification task. In the regression task, the best performance was achieved by Nearest Neighbors. One of the most interesting results is the positive classification of most reviews by VADER: reviewers present constructively written reviews without highly negative comments land; therefore, VADER cannot detect reviews with a negative score. |
| Databáze: | OpenAIRE |
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