Two-Level Multimodal Fusion for Sentiment Analysis in Public Security

• Published in: Security and communication networks Vol. 2021; pp. 1 - 10
• Main Authors: Sun, Jianguo, Yin, Hanqi, Tian, Ye, Wu, Junpeng, Shen, Linshan, Chen, Lei
• Format: Journal Article
• Language: English
• Published: London Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Ablation; Algorithms; Artificial intelligence; Classification; Crime; Data analysis; Data mining; Decision analysis; Empirical analysis; Internet; Machine learning; Medical diagnosis; Medical research; Neural networks; Performance evaluation; Security; Sentiment analysis; Tensors; Wavelet transforms
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2021/6662337

Large amounts of data are widely stored in cyberspace. Not only can they bring much convenience to people’s lives and work, but they can also assist the work in the information security field, such as microexpression recognition and sentiment analysis in the criminal investigation. Thus, it is of great significance to recognize and analyze the sentiment information, which is usually described by different modalities. Due to the correlation among different modalities data, multimodal can provide more comprehensive and robust information than unimodal in data analysis tasks. The complementary information from different modalities can be obtained by multimodal fusion methods. These approaches can process multimodal data through fusion algorithms and ensure the accuracy of the information used for subsequent classification or prediction tasks. In this study, a two-level multimodal fusion (TlMF) method with both data-level and decision-level fusion is proposed to achieve the sentiment analysis task. In the data-level fusion stage, a tensor fusion network is utilized to obtain the text-audio and text-video embeddings by fusing the text with audio and video features, respectively. During the decision-level fusion stage, the soft fusion method is adopted to fuse the classification or prediction results of the upstream classifiers, so that the final classification or prediction results can be as accurate as possible. The proposed method is tested on the CMU-MOSI, CMU-MOSEI, and IEMOCAP datasets, and the empirical results and ablation studies confirm the effectiveness of TlMF in capturing useful information from all the test modalities.