Journal ArticleDOI
Multimodal Event-Aware Network for Sentiment Analysis in Tourism
TLDR
In this paper, a multimodal event-aware network is proposed to analyze sentiment from Weibos that contain multiple modalities, i.e., text and images, to obtain more discriminative representations, based on which they simultaneously perceive the event and sentiment in a multitask framework.Abstract:
Considering the application of a sentiment analysis in decision-making and personalized advertising, we adopt it in tourism. Specifically, we perform a sentiment analysis on the posted Weibos about the passengers’ experience in civil aviation travel. Different travel events could influence passengers’ sentiment, e.g., flight delay may cause negative sentiment. Inspired by this observation, we propose a novel multimodal event-aware network to analyze sentiment from Weibos that contain multiple modalities, i.e., text and images. We first extract features from each modality and, then, model the cross-modal associations to obtain more discriminative representations, based on which we simultaneously perceive the event and sentiment in a multitask framework. Extensive experiments demonstrate that the proposed method outperforms the existing state-of-the-art approaches.read more
Citations
More filters
Proceedings ArticleDOI
A Transformer-based Approach for Identifying Target-oriented Opinions from Travel Reviews
TL;DR: This paper formulates a novel research topic of identifying target-opinion pair from Chinese travel review corpus and leverages aspect-based query, pos-tag and relative position and devise appropriate structure to fuse them in an encoder-decoder framework.
Proceedings ArticleDOI
A Transformer-based Approach for Identifying Target-oriented Opinions from Travel Reviews
TL;DR: Zhang et al. as discussed by the authors formulates a novel research topic of identifying target-opinion pair from Chinese travel review corpus and devise appropriate structure to fuse them in an encoder-decoder framework.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Proceedings ArticleDOI
Convolutional Neural Networks for Sentence Classification
TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
Proceedings Article
Recurrent convolutional neural networks for text classification
TL;DR: A recurrent convolutional neural network is introduced for text classification without human-designed features to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks.