Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Enriching Multimodal Sentiment Analysis Through Textual Emotional Descriptions of Visual-Audio Content
Authors: Sheng Wu, Dongxiao He, Xiaobao Wang, Longbiao Wang, Jianwu Dang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on widely used sentiment analysis benchmark datasets, including MOSI, MOSEI, and CH-SIMS, underscore significant enhancements compared to state-of-the-art models. Moreover, fine-grained emotion experiments corroborate the robust sensitivity of DEVA to subtle emotional variations. |
| Researcher Affiliation | Academia | 1School of New Media and Communication, Tianjin University, Tianjin, China 2Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China 3Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, China 4Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the method using diagrams and textual descriptions of modules like EDG and TPF, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'https://github.com/thuiar/MMSA/blob/master/results/resultstat.md' in reference to results obtained from other works, but does not provide a specific repository link or an explicit statement about the release of their own source code for the described methodology. |
| Open Datasets | Yes | We conduct extensive experiments on three standard multimodal sentiment analysis benchmarks: MOSI (Zadeh et al. 2016a), MOSEI (Bagher Zadeh et al. 2018), and CH-SIMS (Yu et al. 2020). |
| Dataset Splits | No | The paper refers to "three standard multimodal sentiment analysis benchmarks: MOSI, MOSEI, and CH-SIMS," which are commonly used with predefined splits. However, it does not explicitly provide the specific training, validation, or test split percentages or sample counts within the main text. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU models, CPU types, or memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions several tools and models like BERT, Librosa, Open Face, and Open SMILE, but does not specify their version numbers or other software dependencies required for replication. |
| Experiment Setup | No | The paper mentions using standard cross-entropy loss for classification and mean squared error (MSE) loss for regression. However, it does not provide specific details on hyperparameters such as learning rate, batch size, number of epochs, or optimizer settings for the experimental setup. |