Towards multimodal sentiment analysis: harvesting opinions from the web

By Louis-philippe Morency, Rada Mihalcea, Payal Doshi. International Conference on Multimodal Interaction, 2011

556 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology

View Paper: https://www.semanticscholar.org/paper/0cbeb3cce2947fec2f790b1a28fd182640251b4e

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Citations in the corpus

Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities (2018, unknown): “We additionally 211 perform experiments on the ICT-MMMO [62] and YouTube [34] datasets.”
Multi-attention Recurrent Network for Human Communication Comprehension (2018, methodology): “We employ three modalities in a coordinated manner to convey our intentions: language modality (words, phrases and sentences), vision modality (gestures and expressions), and acoustic modality (paralinguistics and changes in vocal tones) (Morency, Mihalcea, and Doshi 2011).”
MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos (2016, methodology): “Multimodal sentiment analysis datasets YouTube Opinion Dataset created by Morency et.al.”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, methodology): “[133] pioneered multimodal sentiment analysis by proposing a tri-modal sentiment analysis architecture for Spanish.”
Multimodal Language Analysis with Recurrent Multistage Fusion (2018, background): “Each modality is represented by averaging temporal information through time, as shown for language-based sentiment analysis (Iyyer et al., 2015; Chen et al., 2016) and multimodal sentiment analysis (Abburi et al., 2016; Nojavanasghari et al., 2016; Zadeh et al., 2016; Morency et al., 2011).”
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality (2020, background): “, 2008), YouTube (Morency et al., 2011), MOUD (Pérez-Rosas et al.”
Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis (2015, methodology): “We experimented on the dataset described by Morency et al. (2011).”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “Harvesting Opinions from the Web database (HOW) [104] is the first publicly available database containing visual, audio and textual modalities for sentiment analysis.”
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks (2015, background): “Only a few recent works attempted to predict visual sentiment using features from images (Siersdorfer et al. 2010; Borth et al. 2013b; Borth et al. 2013a; Yuan et al. 2013) and videos (Morency, Mihalcea, and Doshi 2011).”
Memory Fusion Network for Multi-view Sequential Learning (2018, methodology): “We follow the implementation in (Morency, Mihalcea, and Doshi 2011) for tri-modal data.”
Efficient Low-rank Multimodal Fusion With Modality-Specific Factors (2018, methodology): “…recognition (Yuhas et al., 1989), emotion recognition, (De Silva et al., 1997), (Chen et al., 1998), (W¨ollmer et al., 2013), sentiment analysis, (Morency et al., 2011) ∗ equal contributions as well as speaker trait analysis and media description (Park et al., 2014a) have seen a great boost in…”
Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, background): “For more information about other types of polarity classification, one can refer to Morency et al. [2011]. Automatic classification of polarity can be categorized with respect to various perspectives.”
Bayesian network based extreme learning machine for subjectivity detection (2017, methodology): “Multimodal opinion utterances dataset MOUD consists of 498 short video fragments containing one sentence each.”
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception (2017, background): “This section overviews techniques to record emotional databases, emphasizing the role of recordings collected by actors.”