@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}, }