Multimodal Emotion Recognition and Fluctuation: A Study on Sentiment Analysis of Online Public Opinion

Abstract

Emotion is an important way for individuals to express their views on the Internet and an important variable that shapes public opinion. Considering the multimodality of data, such as text, picture and video, and the subtlety of emotional expression, a multimodal sentiment analysis model that addresses content involving difference senses, such as sight, hearing and touch at the same time is very necessary. This study outlines the basic steps, classification strategies and research methods of sentimental analysis and acknowledges the differences between sentimental analyses on text, picture and video. As multimodal sentiment recognition is still in its initial stage, there’s still room for improvement in cross-disciplinary research on multimodal data of text, picture, audio, video in terms of weighted scoring, complex emotion and intensity recognition. It’s concluded that future studies should focus on the intensity of different emotions, multimodal data fusion and how weighted scoring influences an emotion recognition model and explore application possibilities.