Multimodal Sentiment Analysis

TL;DR: A recurrent neural network based multi-modal attention framework that leverages the contextual information for utterance-level sentiment prediction that applies attention on multi- modal multi-utterance representations and tries to learn the contributing features amongst them.

TL;DR: The main challenge in multimodal sentiment analysis is the integration of cross-modal sentiment information, so as discussed by the authors focus on introducing the framework and characteristics of different fusion methods and discuss the development status, popular datasets, feature extraction algorithms, application areas, and existing challenges.

TL;DR: A recurrent neural network based approach for the multi-modal sentiment and emotion analysis that learns the inter- modal interaction among the participating modalities through an auto-encoder mechanism.

TL;DR: A detailed overview of the latest trends in research pertaining to visual and language modalities is presented in this paper , where the authors look at their applications in their task formulations and how to solve various problems related to semantic perception and content generation.

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.

TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.

TL;DR: Two novel model architectures for computing continuous vector representations of words from very large data sets are proposed and it is shown that these vectors provide state-of-the-art performance on the authors' test set for measuring syntactic and semantic word similarities.

TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.