  Targeted sentiment analysis (TSA), also known as aspect based sentiment
analysis (ABSA), aims at detecting fine-grained sentiment polarity towards
targets in a given opinion document. Due to the lack of labeled datasets and
effective technology, TSA had been intractable for many years. The newly
released datasets and the rapid development of deep learning technologies are
key enablers for the recent significant progress made in this area. However,
the TSA tasks have been defined in various ways with different understandings
towards basic concepts like `target' and `aspect'. In this paper, we categorize
the different tasks and highlight the differences in the available datasets and
their specific tasks. We then further discuss the challenges related to data
collection and data annotation which are overlooked in many previous studies.
