@article{(Open Science Index):https://publications.waset.org/pdf/10012408,
	  title     = {Fine-Grained Sentiment Analysis: Recent Progress},
	  author    = {Jie Liu and  Xudong Luo and  Pingping Lin and  Yifan Fan},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {16},
	  number    = {2},
	  year      = {2022},
	  pages     = {21 - 30},
	  ee        = {https://publications.waset.org/pdf/10012408},
	  url   	= {https://publications.waset.org/vol/182},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 182, 2022},
	}