Computational Analysis of Persuasiveness in Social Multimedia: A Novel Dataset and Multimodal Prediction Approach

By Sunghyun Park, H. Shim, Moitreya Chatterjee, Kenji Sagae, Louis-philippe Morency. 2014

152 citations recorded by the source corpus.

Subfields: Literature and Literary Theory, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Towards Understanding and Mitigating Social Biases in Language Models (2021, background): “…5 real-world text corpora spanning W IKI T EXT -2 (Merity et al., 2017), SST (Socher et al., 2013), R EDDIT , MELD (Poria et al., 2019), and POM (Park et al., 2014) which cover both spoken and written English language across formal and informal settings and a variety of topics (Wikipedia,…”
Towards Debiasing Sentence Representations (2020, methodology): “Datasets: We used the following five datasets: a) WikiText-2 [15], a dataset of formally written Wikipedia articles commonly used as a benchmark for language models (we only use the first 10% of WikiText-2 which we found to be sufficient to capture formally written text), b) Stanford Sentiment Treeb”
Multi-attention Recurrent Network for Human Communication Comprehension (2018, methodology): “SVM (Support Vector Machine) (Cortes and Vapnik 1995) a SVM is trained on the concatenated multimodal features for classiﬁcation or regression (Zadeh et al. 2016; Perez-Rosas, Mihalcea, and Morency 2013b; Park et al. 2014).”
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (2018, unknown): “…(Poria et al., 2017), ♣ C-MKL (Poria et al., 2016), ♭ DF (Nojavanasghari et al., 2016), ♡ SVM (Cortes and Vapnik, 1995; Zadeh et al., 2016b; Perez-Rosas et al., 2013; Park et al., 2014), ● RF (Breiman, 2001), THMM (Morency et al., 2011), SAL-CNN (Wang et al., 2016), 3D-CNN (Ji et al., 2013).”
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors (2018, methodology): “This baseline is trained on the concatenated multimodal features for clas-siﬁcation or regression tasks (P ´ erez-Rosas, Mihalcea, and Morency 2013; Park et al. 2014; Zadeh et al. 2016).”
Factorized Multimodal Transformer for Multimodal Sequential Learning (2019, methodology): “We perform experiments for discrete emotions (Ekman, 1992) (Zadeh et al., 2017) 24.1 31.0 31.5 34.5 24.6 25.6 27.6 29.1 MARN (Zadeh et al., 2018b) 29.1 33.0 - - 31.5 - - - MFN (Zadeh et al., 2018a) 34.5 35.5 37.4 41.9 34.5 36.9 36.0 37.9 RMFN (Liang et al., 2018) 37.4 38.4 37.4 - 37.4 38.9 38.9 - Mu”
Multimodal Language Analysis with Recurrent Multistage Fusion (2018, background): “, 2011), SVM (Cortes and Vapnik, 1995; Park et al., 2014) and RF (Breiman, 2001).”
Memory Fusion Network for Multi-view Sequential Learning (2018, unknown): “The POM dataset (Park et al. 2014) contains 1,000 movie review videos.”
Multimodal sentiment analysis with word-level fusion and reinforcement learning (2017, methodology): “Besides sentiment, other speakers’ attributes such as persuasion, passion and confidence could also be analyzed by similar methods [15, 18].”
Integrating Multimodal Information in Large Pretrained Transformers (2020, methodology): “These analyses have particularly focused on the tasks of sentiment analysis (Poria et al., 2018), emotion recognition (Zadeh et al., 2018d), and personality traits recognition (Park et al., 2014).”