태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Multimodal sentiment analysis is the task of performing sentiment analysis with multiple data sources - e.g. a camera feed of someone's face and their recorded speech.
<span style="color:grey; opacity: 0.6">( Image credit: ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection )</span>
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
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.
Multimodal Opinionlevel Sentiment Intensity (MOSI) contains: (1) multimodal observations including transcribed speech and visual gestures as well as automatic audio and visual features, (2) opinion-level subjectivity segmentation, (3) sentiment intensity annotations with high coder agreement, and (4) alignment between words, visual and acoustic features. Source: Zadeh et al Image source: Zadeh et al
CH-SIMS is a Chinese single- and multimodal sentiment analysis dataset which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis. Source: CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotations of Modality
The Multimodal Corpus of Sentiment Intensity (CMU-MOSI) dataset is a collection of 2199 opinion video clips. Each opinion video is annotated with sentiment in the range [-3,3]. The dataset is rigorously annotated with labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, and per-milliseconds annotated audio features.
The MuSe-CAR database is a large, multimodal (video, audio, and text) 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. The estimated age range of the professional, semi-professional (influncers), and casual reviewers is between the middle of 20s until the late 50s. Most are native English speakers from the UK or the US, while a small minority are non-native, yet fluent English speakers.
A multimodal dataset for sentiment analysis on internet memes.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.