CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) is the largest dataset of sentence-level sentiment analysis and emotion recognition in online videos. CMU-MOSEI contains over 12 hours of annotated video from over 1000 speakers and 250 topics.

CMU-MOSEI is a comprehensive multimodal dataset designed to analyze emotions and sentiment in online videos. It's a valuable resource for researchers and developers working on automatic emotion recognition and sentiment analysis.

Key Features: Over 23,500 video clips from 1000+ speakers, covering diverse topics and monologues.

Multimodal data:

Acoustics: Features extracted from audio (CMU_MOSEI_COVAREP.csd) Labels: Annotations for sentiment intensity and emotion categories (CMU_MOSEI_Labels.csd) Language: Phonetic, word-level, and word vector representations (CMU_MOSEI_*.csd files under languages folder)

Visuals: Features extracted from facial expressions (CMU_MOSEI_Visual*.csd files under visuals folder)

Balanced for gender: The dataset ensures equal representation from male and female speakers.

Unlocking Insights: By exploring the various modalities within CMU-MOSEI, researchers can investigate the relationship between speech, facial expressions, and emotions expressed in online videos.

Download: The dataset is freely available for download at: http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/

Start exploring the world of emotions in videos with CMU-MOSEI!

CMU-MOSEI: Computational Sequences (Unofficial Mirror)

This repository provides a mirror of the official computational sequence files from the CMU-MOSEI dataset, which are required for multimodal sentiment and emotion research. The original download links are currently down, so this mirror is provided for the research community.

Note: This is an unofficial mirror. All data originates from Carnegie Mellon University and original authors. If you are a dataset creator and want this… See the full description on the dataset page: https://huggingface.co/datasets/reeha-parkar/cmu-mosei-comp-seq.

CMU-MOSEI dataset information.

CMU-MOSEI数据集在CMU-MOSI的基础上扩展了数据量，包括了来自5000个视频的22856个视频片段，并且丰富了说话者的多样性，涵盖了更多主题。其中每个片段都是一个独立的多模态示例，其中图像、文本和音频占比也是均匀的，同样被标记为[-3,+3]的情感得分。The CMU-MOSEI dataset expands the data base of the CMU-MOSI to include 22,856 video clips from 5,000 videos, as well as enriching the diversity of speakers and covering more topics. Each segment is an independent multimodal example with an even proportion of image, text, and audio, also labeled with an emotion score of [-3,+3].

This refers to the feature vectors obtained after feature extraction from the multimodal datasets IEMOCAP, MELD, CMU-MOSEI, Twitter2019, CrisisMMD, and DMD.

Comparative experiments of multimodal sentiment analysis models on the dataset CMU-MOSEI.

Comparative experiments of multimodal sentiment analysis models on the dataset CMU-MOSEI.