Research on Lovelorn Emotion Recognition Based on Ernie Tiny
Yuxin Huang · Frontiers in Computing and Intelligent Systems · 2023
Topics related to sentiment classification and emotion recognition are an important part of the Natural Language Processing research field and can be used to analyze users' sentiment tendencies towards brands, understand the public's attitudes and opinions on public opinion events, and detect users' mental health, among others. Past research has usually been based on positive and negative emotions or multi-categorized emotions such as happiness, anger and sadness, while there has been little research on the recognition of the specific emotion of lovelorn. This study aims to identify the lovelorn emotion in text, using deep learning pretrained model ERNIR Tiny to train a dataset consisting of 5008 pieces of Chinese lovelorn emotion text crawled from social media platform Weibo and 4998 pieces of ordinary text extracted from existing available dataset. And finally, it was proved that ERNIE Tiny performs well in classifying whether a text contains lovelorn emotion or not, with F1 score of 0.941929, precision score of 0.942300 and recall score of 0.941928 obtained on the test set.