Codes for paper:A Prompt-Based Learning Approach for Few-Shot Social Media Depression Detection
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Updated
Feb 5, 2024 - Python
Codes for paper:A Prompt-Based Learning Approach for Few-Shot Social Media Depression Detection
Implementation of DFMR for Multimodal Sentiment Analysis in Malayalam (Native Indian Dravida Language)
visual and textual multimodal sentiment analysis, based on pytorch.
2023 1st semester -BigDataProject Team Project Page
This repository contains the code for submission made at SemEval 2022 Task 5: MAMI
Official Git repository for "Hakimov, S., and Schlangen, D., (2023). Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks. Findings of the Association for Computational Linguistics (ACL 2023 Findings)"
Multimodal Emotion Recognition using ClipBERT.
Multimodal Sentiment Analysis of video reviews on social media platform, using a supervised fuzzy rule-based system.
Open source code for paper: End-to-End Multimodal Emotion Visualization Analysis System
Multimodal emotion recognition on two benchmark datasets RAVDESS and SAVEE from audio-visual information using CNN(Convolutional Neural Networks)
This repository contains the code for the paper "Sentiment-driven statistical causality in multimodal systems", by Ioannis Chalkiadakis, Anna Zaremba, Gareth W. Peters and Michael J. Chantler.
Code and Splits for the paper "A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods", In Proceedings of the 2021 Workshop on Multi-Modal Pre-Training for Multimedia Understanding (MMPT ’21), August 21, 2021,Taipei, Taiwan
Sentiment Analysis, Summarization, Tagging with MongoDB Atlas and Gemini — Google Cloud's AI model
Multimodal sentiment analysis
Emotion recognition methods through facial expression, speeches, audios, and multimodal data
Bimodal and Unimodal Sentiment Analysis of Internet Memes (Image+Text)
DeepCU: Integrating Both Common and Unique Latent Information for Multimodal Sentiment Analysis, IJCAI-19
[EMNLP 2022] This repository contains the official implementation of the paper "MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences"
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