Aspect-based sentiment analysis (ABSA) aims to detect the targets, aspects, and sentiment polarities in text. A published dataset from SemEval-2015 reveals that a sentiment polarity depends on both the target and the aspect. However, most existing methods consider predicting sentiment polarities from either targets or aspects but not from both. Thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. We propose a method for target-aspect-sentiment joint detection to tackle these limitations in ABSA. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. SemEval-2015 restaurant datasets show that the proposed method achieves high-performance detecting target-aspect-sentiment triples even for the implicit target cases.