A survey of multimodal sentiment analysis

By Mohammad Soleymani, David García, Brendan Jou, Björn Schuller, Shih-Fu Chang, Maja Pantic. 2017

609 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Natural Language Processing, Multimodal Learning

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Citations in the corpus

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “Related multimodal classification tasks exist; for instance, there has been extensive research in multimodal sentiment [73], but there is no agreed-upon standard dataset or benchmark task.”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, background): “Sentiment analysis, also known as opinion mining, aims to evoke opinions or sentiments of a person who encounters a speciic topic, person, or entity [1].”
Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions (2022, background): “The early studies [87 – 89] show that employing any combination of three modalities, a bimodal system and trimodal system have superior accuracy than a unimodal system.”
Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts (2019, background): “…2017; Zellers et al., 2018), which assume that the text is a subordinate modality, or on extracting the literal or connotative meaning of a post (Soleymani et al., 2017), or its perlocutionary force (how it is perceived by its audience), including aspects such as memorability (Khosla et al.,…”
Emotional Voice Conversion: Theory, Databases and ESD (2021, background): “One example is Russell’s circumplex model [76, 81], which is defined by arousal, valence, and dominance [82].”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “Soleymani et al. (2017) and Yadav and Vishwakarma (2020) on sentiment classification have been published.”
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis (2019, methodology): “With the explosive growth in availability of data, several machine learning algorithms have been successfully applied towards multimodal tasks such as sentiment analysis (Morency, Mihalcea, and Doshi 2011; Soleymani et al. 2017), emotion recognition (Haq and Jackson 2011), image-text retrieval (Wang”
Sentiment analysis using deep learning architectures: a review (2019, methodology): “Yanagimoto et al. (2013) developed a neural network architecture, which is composed of four layers of RBM sharing hidden layer units and visible units for estimating the similarity between the articles.”
Deep Facial Expression Recognition: A Survey (2018, neutral): “tive) towards a certain entity [214], [215].”