Aspect-based Sentiment Analysis of Scientific Reviews
Table 12. Feature importance study: % drop in classifier performance while dropping each aspect sentiment feature.
['Aspects', '% difference in accuracy']
Results: We obtain 65.3% accuracy after 10-fold cross validation in this task making it apparent that the aspect sentiment features hold useful information about the final recommendation. We further investigate the importance of the sentiment features in our task. We drop one aspect (3 sentiment features, positive, negative and neutral) We note that the sentiments binding to the aspects empirical/theoretical soundness, impact of ideas and clarity are the most effective ones in predicting the intended recommendation in a review. This again confirms the results of the previous section. We believe that this result is particularly important because it acts as a guideline for the authors as to what particular aspects they should focus on while submitting their papers.