An Overview on Fine-grained Text Sentiment Analysis: Survey and Challenges

Abstract

Among various natural language process tasks, sentiment analysis has always been a research hotspot. From the initial sentence-level and document-level coarse-grained sentiment analysis to recent fine-grained sentiment analysis on the aspect word level, researchers are committed to applying diverse methods to obtain better sentiment analysis results, ranging from lexicon-based, statistical machine learning methods to deep learning models. In the change of technology, several benchmark datasets that can be used for model performance comparison are gradually yielded. This article summarizes the current research status of aspect-level text sentiment analysis from multiple dimensions such as dataset, mainstream methods, and evaluation indicators, finally, it puts forward the challenges facing and potential research directions from a unique perspective.