%0 Journal Article
	%A Jie Liu and  Xudong Luo and  Pingping Lin and  Yifan Fan
	%D 2022
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 182, 2022
	%T Fine-Grained Sentiment Analysis: Recent Progress
	%U https://publications.waset.org/pdf/10012408
	%V 182
	%X Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially the fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, ma-chine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.
	%P 21 - 30