**WisdoM+**: Enhancing Multimodal Sentiment Analysis by Integrating Contextual World Knowledge **Authors**: Wenbin Wang, Liang Ding, Li Shen, Yong Luo, Han Hu, Dacheng Tao **Institution**: Wuhan University, The University of Sydney, JD Explore Academy, Beijing Institute of Technology **Abstract**: Sentiment analysis (SA) has advanced significantly by leveraging various data modalities such as text and images. However, most existing methods rely on superficial information, neglecting the integration of contextual world knowledge, which hinders their ability to achieve better multimodal sentiment analysis (MSA). This paper introduces WisdoM+, a plug-and-play framework that leverages large vision-language models (LVMs) to generate comprehensive context, thereby enhancing MSA performance. WisdoM+ consists of three stages: Prompt Templates Generation, Context Generation, and Contextual Fusion. The first stage uses large language models like ChatGPT to generate prompt templates, which are then used in the second stage to generate context from images and texts. The third stage employs a training-free contextual fusion mechanism to mitigate noise in the context. Experiments on multiple benchmarks show that WisdoM+ consistently outperforms state-of-the-art methods, achieving an average F1 score improvement of +1.96%. **Introduction**: MSA aims to identify human sentiment polarity from multimodal data. While existing methods have achieved success, they often rely on superficial information, lacking the incorporation of contextual world knowledge. WisdoM+ addresses this by leveraging LVMs to generate explicit contextual world knowledge, enhancing MSA capabilities. The proposed method includes a novel contextual fusion mechanism to reduce noise in the context, making it more reliable for hard samples. **Methodology**: WisdoM+ follows a three-stage process: 1. **Prompt Templates Generation**: Uses ChatGPT to generate prompt templates. 2. **Context Generation**: Generates context using LVMs based on the provided image and sentence. 3. **Contextual Fusion**: Fuses the predicted probability with the context to improve predictions, especially for hard samples. **Experiments**: WisdoM+ is evaluated on aspect-level and sentence-level MSA tasks using multiple benchmarks and models. Results show significant improvements over existing methods, with a maximum F1 score gain of 6.3%. Ablation studies and ablation studies of different fusion strategies further validate the effectiveness of WisdoM+. **Conclusion**: WisdoM+ is a simple and effective framework that enhances MSA by leveraging contextual world knowledge. It demonstrates robust performance across various benchmarks and models, highlighting the importance of integrating deeper knowledge in MSA tasks.**WisdoM+**: Enhancing Multimodal Sentiment Analysis by Integrating Contextual World Knowledge **Authors**: Wenbin Wang, Liang Ding, Li Shen, Yong Luo, Han Hu, Dacheng Tao **Institution**: Wuhan University, The University of Sydney, JD Explore Academy, Beijing Institute of Technology **Abstract**: Sentiment analysis (SA) has advanced significantly by leveraging various data modalities such as text and images. However, most existing methods rely on superficial information, neglecting the integration of contextual world knowledge, which hinders their ability to achieve better multimodal sentiment analysis (MSA). This paper introduces WisdoM+, a plug-and-play framework that leverages large vision-language models (LVMs) to generate comprehensive context, thereby enhancing MSA performance. WisdoM+ consists of three stages: Prompt Templates Generation, Context Generation, and Contextual Fusion. The first stage uses large language models like ChatGPT to generate prompt templates, which are then used in the second stage to generate context from images and texts. The third stage employs a training-free contextual fusion mechanism to mitigate noise in the context. Experiments on multiple benchmarks show that WisdoM+ consistently outperforms state-of-the-art methods, achieving an average F1 score improvement of +1.96%. **Introduction**: MSA aims to identify human sentiment polarity from multimodal data. While existing methods have achieved success, they often rely on superficial information, lacking the incorporation of contextual world knowledge. WisdoM+ addresses this by leveraging LVMs to generate explicit contextual world knowledge, enhancing MSA capabilities. The proposed method includes a novel contextual fusion mechanism to reduce noise in the context, making it more reliable for hard samples. **Methodology**: WisdoM+ follows a three-stage process: 1. **Prompt Templates Generation**: Uses ChatGPT to generate prompt templates. 2. **Context Generation**: Generates context using LVMs based on the provided image and sentence. 3. **Contextual Fusion**: Fuses the predicted probability with the context to improve predictions, especially for hard samples. **Experiments**: WisdoM+ is evaluated on aspect-level and sentence-level MSA tasks using multiple benchmarks and models. Results show significant improvements over existing methods, with a maximum F1 score gain of 6.3%. Ablation studies and ablation studies of different fusion strategies further validate the effectiveness of WisdoM+. **Conclusion**: WisdoM+ is a simple and effective framework that enhances MSA by leveraging contextual world knowledge. It demonstrates robust performance across various benchmarks and models, highlighting the importance of integrating deeper knowledge in MSA tasks.