Ranking based multi-label classification for sentiment analysis

This paper proposes a sentiment analysis framework based on ranking learning. The framework utilizes BERT model pre-trained on large-scale corpora to extract text features and has two sub-networks for different sentiment analysis tasks. The first sub-network of the framework consists of multiple fully connected layers and intermediate rectified linear units. The main purpose of this sub-network is to learn the presence or absence of various emotions using the extracted text information, and the supervision signal comes from the cross entropy loss function. The other sub-network is a ListNet. Its main purpose is to learn a distribution that approximates the real distribution of different emotions using the correlation between them. Afterwards the predicted distribution can be used to sort the importance of emotions. The two sub-networks of the framework are trained together and can contribute to each other to avoid the deviation from a single network. The framework proposed in this paper has been tested on multiple datasets and the results have shown the proposed framework's potential.