Deep Learning based Fine-grained Sentiment Analysis: A Review
Ziling Hu, Yixuan Zhu, Jiahe Su · 2021
With the recent development of internet technology, the amount of data in social media and online has grown exponentially. It is important for us to conduct Sentiment Analysis upon the massive data that we can collect. While coarse Sentiment Analysis only cares about the polarity of sentiments, it is not sufficient to provide us any more detailed information regarding sentiment and emotion. Among research done in recent years, researchers tend to focus more on Fine-grained Sentiment Analysis, which cares about the polarity of sentiments and the intensity and receptor of sentiments. With the advances of deep learning in recent years and its advantage of independence from manual feature engineering, more and more researchers started to apply it to Sentiment Analysis tasks. This work aims to provide a comparative review of deep learning for Fine-grained Sentiment Analysis tasks to place different approaches in context.