ArticleSensors (Basel, Switzerland)2022
Multimodal Feature Fusion Method for Unbalanced Sample Data in Social Network Public Opinion.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AVaTER: Fusing Audio, Visual, and Textual Modalities Using Cross-Modal Attention for Emotion Recognition.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Human Activity Recognition through Integrated Multimodal Analysis: A Focus on RGB Imaging, Skeletal Tracking, and Pose Estimation.Sensors (Basel, Switzerland) · 2024Article
- Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users' Comments.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
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Abstract
With the wide application of social media, public opinion analysis in social networks has been unable to be met through text alone because the existing public opinion information includes data information of various modalities, such as voice, text, and facial expressions. Therefore multi-modal emotion analysis is the current focus of public opinion analysis. In addition, multi-modal emotion recognition of speech is an important factor restricting the multi-modal emotion analysis. In this paper, the emotion feature retrieval method for speech is firstly explored and the processing method of sample disequilibrium data is then analyzed. By comparing and studying the different feature fusion methods of text and speech, respectively, the multi-modal feature fusion method for sample disequilibrium data is proposed to realize multi-modal emotion recognition. Experiments are performed using two publicly available datasets (IEMOCAP and MELD), which shows that processing multi-modality data through this method can obtain good fine-grained emotion recognition results, laying a foundation for subsequent social public opinion analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.