Incorporating emotional trend into multi-emotion analysis models for long-text sentiment analysis

Abstract

The role of sentiment analysis is vital in natural language processing(NLP) and has garnered significant attention across different domains. However, multi-emotion analysis in long-text is still a challenging task due to the intricate emotional nuances that are conveyed. In this paper, a novel approach for long-text multi-emotion analysis is proposed by integrating emotional trends. This integration aims to enhance the ability of the model to recognize emotions by including word-level sentiment scores as supplementary features. To achieve this, the ISEAR and IMDB datasets are leveraged to investigate the impact of sentiment scores with varying weights on three models: BiLSTM, CNN, and CNN+BiLSTM. The models are trained for 20 and 50 epochs and evaluated by accuracy, precision, recall, F1 score ROC curve and AUC value. The experimental results indicate that the incorporation can improve the processing speed of the multi-emotion analysis task while maintaining performance with a 66.7% probability. The highlighted improvement over the baseline model reduced the time by 33.42%. In the best case, the accuracy of the model increased by 2.26% and the F1 score increased by 2.16% without affecting the running speed.