The IEMOCAP (Busso et al., 2008) contains the acts of 10 speakers in a two-way conversation segmented into utterances. The medium of the conversations in all the videos is English. The database contains the following categorical labels: anger, happiness, sadness, neutral, excitement, frustration, fear, surprise, and other.
Monologue:
| Model | Accuracy | Paper / Source |
|---|---|---|
| CHFusion (Poria et al., 2017) | 76.5% | Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling |
| bc-LSTM (Poria et al., 2017) | 74.10% | Context-Dependent Sentiment Analysis in User-Generated Videos |
Conversational: Conversational setting enables the models to capture emotions expressed by the speakers in a conversation. Inter speaker dependencies are considered in this setting.
| Model | Weighted Accuracy (WAA) | Paper / Source |
|---|---|---|
| CMN (Hazarika et al., 2018) | 77.62% | Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos |
| Memn2n | 75.08 | Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos |
Mohammad et. al, 2016 created a dataset of verb-noun pairs from WordNet that had multiple senses. They annoted these pairs for metaphoricity (metaphor or not a metaphor). Dataset is in English.
| Model | F1 Score | Paper / Source | Code |
|---|---|---|---|
| 5-layer convolutional network (Krizhevsky et al., 2012), Word2Vec | 0.75 | Shutova et. al, 2016 | Unavailable |
Tsvetkov et. al, 2014 created a dataset of adjective-noun pairs that they then annotated for metaphoricity. Dataset is in English.
| Model | F1 Score | Paper / Source | Code |
|---|---|---|---|
| 5-layer convolutional network (Krizhevsky et al., 2012), Word2Vec | 0.79 | Shutova et. al, 2016 | Unavailable |
The MOSI dataset (Zadeh et al., 2016) is a dataset rich in sentimental expressions where 93 people review topics in English. The videos are segmented with each segments sentiment label scored between +3 (strong positive) to -3 (strong negative) by 5 annotators.
| Model | Accuracy | Paper / Source |
|---|---|---|
| bc-LSTM (Poria et al., 2017) | 80.3% | Context-Dependent Sentiment Analysis in User-Generated Videos |
| MARN (Zadeh et al., 2018) | 77.1% | Multi-attention Recurrent Network for Human Communication Comprehension |