TY - JFULL AU - Jie Liu and Xudong Luo and Pingping Lin and Yifan Fan PY - 2022/3/ TI - Fine-Grained Sentiment Analysis: Recent Progress T2 - International Journal of Computer and Information Engineering SP - 20 EP - 30 VL - 16 SN - 1307-6892 UR - https://publications.waset.org/pdf/10012408 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 182, 2022 N2 - 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鈥檚 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. ER -