Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions
Sentiment analysis (SA) has gained much traction In the field of artificial intelligence (AI) and
natural language processing (NLP). There is growing demand to automate analysis of user …
natural language processing (NLP). There is growing demand to automate analysis of user …
A systematic review on affective computing: Emotion models, databases, and recent advances
Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …
A review of affective computing: From unimodal analysis to multimodal fusion
Affective computing is an emerging interdisciplinary research field bringing together
researchers and practitioners from various fields, ranging from artificial intelligence, natural …
researchers and practitioners from various fields, ranging from artificial intelligence, natural …
Long dialogue emotion detection based on commonsense knowledge graph guidance
Dialogue emotion detection is always challenging due to human subjectivity and the
randomness of dialogue content. In a conversation, the emotion of each person often …
randomness of dialogue content. In a conversation, the emotion of each person often …
A review of human activity recognition methods
Recognizing human activities from video sequences or still images is a challenging task due
to problems, such as background clutter, partial occlusion, changes in scale, viewpoint …
to problems, such as background clutter, partial occlusion, changes in scale, viewpoint …
A review and meta-analysis of multimodal affect detection systems
SK D'mello, J Kory - ACM computing surveys (CSUR), 2015 - dl.acm.org
Affect detection is an important pattern recognition problem that has inspired researchers
from several areas. The field is in need of a systematic review due to the recent influx of …
from several areas. The field is in need of a systematic review due to the recent influx of …
Learning affective features with a hybrid deep model for audio–visual emotion recognition
Emotion recognition is challenging due to the emotional gap between emotions and audio-
visual features. Motivated by the powerful feature learning ability of deep neural networks …
visual features. Motivated by the powerful feature learning ability of deep neural networks …
Attentive convolutional neural network based speech emotion recognition: A study on the impact of input features, signal length, and acted speech
Speech emotion recognition is an important and challenging task in the realm of human-
computer interaction. Prior work proposed a variety of models and feature sets for training a …
computer interaction. Prior work proposed a variety of models and feature sets for training a …
Behavioral signal processing: Deriving human behavioral informatics from speech and language
S Narayanan, PG Georgiou - Proceedings of the IEEE, 2013 - ieeexplore.ieee.org
The expression and experience of human behavior are complex and multimodal and
characterized by individual and contextual heterogeneity and variability. Speech and …
characterized by individual and contextual heterogeneity and variability. Speech and …
Learning in audio-visual context: A review, analysis, and new perspective
Sight and hearing are two senses that play a vital role in human communication and scene
understanding. To mimic human perception ability, audio-visual learning, aimed at …
understanding. To mimic human perception ability, audio-visual learning, aimed at …