Multimodal Aspect-Level Sentiment Analysis based on Deep Neural Networks

Abstract

Aspect-level sentiment analysis is a fine-grained task of sentiment analysis that aims to identify the sentiment polarity of specific aspect words in a sentence. However, most existing approaches rely mainly on text content and ignore other potentially useful data (e.g., images) that can complement the text content and thus more accurately represent the user's sentiment. Therefore, we focus on Aspect-level Multi-model Sentiment Analysis(AMSA). Analyzing the current research status of AMSA, explore the independence and correlation between multiple modal data, capture their deep interaction information, and improve the data feature analysis and fusion capability from feature extraction methods and feature fusion strategies, respectively. Finally, the corresponding modeling scheme is proposed for aspect-level multimodal aspect-level sentiment analysis.