Imbalanced Sentiment Classification Enhanced with Discourse Marker
Table 1: Experimental results of several methods in different settings. The evaluation metric is accuracy (%). “raw” denotes raw imbalanced sentiment datasets. “w/” represents “with”. “os” denotes “oversampling”. “our” denotes “our method”.
['Method', 'Setting', 'MR', 'SST2', 'CR', 'Avg improvement']
Following the experiment settings introduced in Section 4.2, we test the performance of listed methods. with imbalanced-ratio λ=5. Our method generates more samples and builds a more balanced dataset for further oversampling, consistently increasing the classification performance. It worthy to note that SVM performs unstable, even unchanged in CR dataset. We ascribe this to the special modeling process of SVM, which is not the focus of this paper. Since our propose is to verify the performance of our data augmentation method, we remove SVM in following experiments for comparison convenience.