Summary of Target-dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-caption Translation Network Using Visual-caption Pairs, by Ananya Pandey et al.
Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairs
by Ananya Pandey, Dinesh Kumar Vishwakarma
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study aims to develop a novel approach for Target-Dependent Multimodal Sentiment Analysis (TDMSA) that incorporates emotional clues from facial expressions. The goal is to analyze sentiment associated with every target (aspect) in multimodal posts, combining visual and textual content. A new technique called the Visual-to-Emotional-Caption Translation Network (VECTN) is introduced, focusing on acquiring visual sentiment clues by analyzing facial expressions. The methodology is evaluated on two publicly available Twitter datasets, achieving an accuracy of 81.23% and a macro-F1 of 80.61% on the Twitter-15 dataset, and 77.42% and 75.19% on the Twitter-17 dataset. This study demonstrates the effectiveness of the proposed model in collecting target-level sentiment in multimodal data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to understand how people feel about certain things when they post pictures with captions online. They used special computers to look at people’s faces and figure out what emotions they were showing. Then, they combined that information with the words people wrote in their captions. The new method worked really well on two big datasets of Twitter posts. |
Keywords
» Artificial intelligence » Translation
