Multi-modal fusion attention sentiment analysis for mixed sentiment classification

Abstract

Mixed sentiment classification (MSC) technology has a significant research value and application potential in understanding and analysing sentimental interactions. In the process of identifying and analysing complex sentiments, it is still necessary to overcome the difficulties of multi-dimensional sentiment recognition and improve sensitivity to subtle sentimental differences. Therefore, a multi-modal fusion attention sentiment analysis based on MSC to address this challenge is proposed. Firstly, the sentiment analysis fusion strategy based on multi-modal fusion is studied, which can fully utilise the information of multi-modal inputs such as text, audio, and video, thereby gaining a more comprehensive understanding and recognition of sentiments. Secondly, a sentiment analysis model based on multi-modal fusion attention is constructed, which focuses on the key information of multi-modal inputs to achieve an accurate recognition of mixed sentiments. The experimental results show that the proposed method outperforms existing sentiment analysis methods on both datasets, with F1 values of 83.17 and 84.19, accuracy of 39.15 and 39.98, and errors of 0.516 and 0.524, respectively. The accuracy range is 95.38%–99.89%, verifying the superiority of the method in sentiment analysis. It can be seen that this method provides a more effective and reliable MSC solution, which has practical significance for improving the accuracy and recall of sentiment analysis.