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Related Experiment Video

Updated: May 20, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

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A multi-modal sarcasm detection model based on cue learning.

Ming Lu^1,2, Zhiqiang Dong³, Ziming Guo¹

¹School of Cyber Science and Technology, Beihang University, Beijing, 100191, China.

Scientific Reports

|March 26, 2025

Summary

This study introduces a new multi-modal sarcasm detection model using cue learning for better sentiment analysis, especially in low-resource settings. The model effectively integrates text and image data for improved accuracy in detecting sarcasm.

Keywords:

Cue Learning Low-Resource Languages Multi-modal Learning Public Opinion Monitoring Sarcasm Detection Sentiment Analysis

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Area of Science:

Natural Language Processing
Computer Vision
Artificial Intelligence

Background:

The increasing volume of online data necessitates advanced sentiment analysis techniques.
Sarcasm detection is a challenging aspect of sentiment analysis, particularly in low-resource languages due to data scarcity.

Purpose of the Study:

To develop a novel multi-modal sarcasm detection model.
To address data scarcity challenges in sarcasm detection using cue learning.
To enhance sentiment analysis accuracy by integrating text and image modalities.

Main Methods:

Utilized the CLIP architecture for multi-modal learning.
Employed discrete prompt generation and learnable continuous vectors.
Implemented a symmetric multi-modal fusion process for text and image data integration.

Main Results:

Achieved significant performance improvements on the Twitter Multi-modal Sarcasm Detection Dataset (MSD).
Demonstrated robustness and adaptability in small-sample scenarios.
Outperformed traditional sarcasm detection models.

Conclusions:

The proposed multi-modal model offers a practical solution for nuanced sentiment analysis.
The cue learning approach effectively handles data scarcity in sarcasm detection.
This research advances public opinion monitoring and AI decision-making through improved sentiment analysis.