Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the volatile nature of spoken language in online videos as well as accompanying gestures and voice. In the experiments, our model outperforms state-of-the-art approaches for both multimodal and unimodal sentiment analysis.
is an emerging research area that integrates verbal and nonverbal behaviors into the detection of user sentiment. There exist several multimodal datasets that include sentiment annotations, including the newly-introduced CMU-MOSI dataset @cite_7 , as well as other datasets including ICT-MMMO @cite_55 , YouTube @cite_12 , and MOUD @cite_53 , however CMU-MOSI is the only English dataset with utterance-level sentiment labels. The newest multimodal sentiment analysis approaches have used deep neural networks, including convolutional neural networks (CNNs) with multiple-kernel learning @cite_25 , SAL-CNN @cite_33 which learns generalizable features across speakers, and support vector machines (SVMs) with a multimodal dictionary @cite_14 .
@cite_14 @cite_33 @cite_7 @cite_55 @cite_53 @cite_25 @cite_12
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We present a novel way of extracting features from short texts, based on the activation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal sentiment analysis of short video clips representing one sentence each. We use the combined feature vectors of textual, visual, and audio modalities to train a classifier based on multiple kernel learning, which is known to be good at heterogeneous data. We obtain 14 performance improvement over the state of the art and present a parallelizable decision-level data fusion method, which is much faster, though slightly less accurate. People are sharing their opinions, stories and reviews through online video sharing websites every day. Studying sentiment and subjectivity in these opinion videos is experiencing a growing attention from academia and industry. While sentiment analysis has been successful for text, it is an understudied research question for videos and multimedia content. The biggest setbacks for studies in this direction are lack of a proper dataset, methodology, baselines and statistical analysis of how information from different modality sources relate to each other. This paper introduces to the scientific community the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos called Multimodal Opinion-level Sentiment Intensity dataset (MOSI). The dataset is rigorously annotated with labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, and per-milliseconds annotated audio features. Furthermore, we present baselines for future studies in this direction as well as a new multimodal fusion approach that jointly models spoken words and visual gestures. People share their opinions, stories, and reviews through online video sharing websites every day. The automatic analysis of these online opinion videos is bringing new or understudied research challenges to the field of computational linguistics and multimodal analysis. Among these challenges is the fundamental question of exploiting the dynamics between visual gestures and verbal messages to be able to better model sentiment. This article addresses this question in four ways: introducing the first multimodal dataset with opinion-level sentiment intensity annotations; studying the prototypical interaction patterns between facial gestures and spoken words when inferring sentiment intensity; proposing a new computational representation, called multimodal dictionary, based on a language-gesture study; and evaluating the authors' proposed approach in a speaker-independent paradigm for sentiment intensity prediction. The authors' study identifies four interaction types between facial gestures and verbal content: neutral, emphasizer, positive, and negative interactions. Experiments show statistically significant improvement when using multimodal dictionary representation over the conventional early fusion representation (that is, feature concatenation). Multimodal sentiment analysis is drawing an increasing amount of attention these days. It enables mining of opinions in video reviews and surveys which are now available aplenty on online platforms like YouTube. However, the limited number of high-quality multimodal sentiment data samples may introduce the problem of the sentiment being dependent on the individual specific features in the dataset. This results in a lack of generalizability of the trained models for classification on larger online platforms. In this paper, we first examine the data and verify the existence of this dependence problem. Then we propose a Select-Additive Learning (SAL) procedure that improves the generalizability of trained discriminative neural networks. SAL is a two-phase learning method. In Selection phase, it selects the confounding learned representation. In Addition phase, it forces the classifier to discard confounded representations by adding Gaussian noise. In our experiments, we show how SAL improves the generalizability of state-of-the-art models. We increase prediction accuracy significantly in all three modalities (text, audio, video), as well as in their fusion. We show how SAL, even when trained on one dataset, achieves good accuracy across test datasets.