Multimodal Sentiment Analysis in Car Reviews dataset - raw data (MuSe-CAR raw)

General: The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.

概述：真实媒体多模态情感分析挑战赛（Multimodal Sentiment Analysis in Real-life media Challenge，简称MuSe）的举办宗旨是汇聚不同学科的研究社群，主要涵盖基于信号的视听情感识别研究社群与基于符号的情感分析研究社群。我们推出了全新数据集MuSe-CAR，其覆盖了上述所有预期需求范畴。

MuSe-CAR是一个时长超过36小时的大型多模态数据集，通过真实场景（in-the-wild）采集获取，旨在进一步探索真实场景下的多模态情感分析——例如产品评测（即汽车评测）中与主题或实体绑定的情感相关的情感投入状态。考虑到近年来日常录制设备性能不断提升，同时社交媒体视频内容的信息量日益丰富，我们在设计MuSe-CAR时兼顾了高音质与高画质。该数据集能够支持模型开展鲁棒性较强的训练任务，即便面对大量真实场景下的新颖特性，例如：
i) 视频维度：镜头景别（包含特写、中景、远景的混合组合）、人脸拍摄角度（侧面、眼部特写、低角度、高角度）、摄像机运动模式（自由移动、稳定自由移动、不稳定自由移动、切换模式如变焦、固定机位）、评测者出镜状态（全身、半身、仅面部、仅手部）、高度多变的背景，以及人物与物体（汽车零部件）的互动场景。
ii) 音频维度：环境噪声（汽车噪音、背景音乐）、旁白与主持人的说话人分割标注（diarisation）、多样化的麦克风类型，以及声源位置差异。
iii) 文本维度：口语化表达与领域专属术语。

搜集汇总

背景与挑战

背景概述

MuSe-CAR raw是一个大型多模态数据集，包含超过36小时的汽车评论数据，涵盖音频、视频和文本模态，旨在促进多模态情感分析的研究。数据集以其高质量和多样化的真实场景数据为特点，适用于研究情感分析在复杂环境下的表现。