Multimodal Event-Aware Network for Sentiment Analysis in Tourism

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.