Weighted Pooling RoBERTa for Effective Text Emotion Detection

Textual emotion detection is a classification problem that assigns different emotions to a given text input. It reveals the writer’s mental state. Its diversity and uncertainty make it a challenging task. The existing methods in machine learning can be used for emotion detection; however, it fails in processing very long passages. In this work, we employ weighted pooling pretrained RoBERTa model for effective textual emotion detection. To perform experiments, we use two data sets, ISEAR and tweets, with 7516 and 21048 records, respectively. Precision, recall, F1-score, and classification accuracy are used to assess the models. Experimental results indicate that the weighted pooling RoBERTa model outperforms the deep learning models on both datasets with significant improvement in accuracy.