Aspect-based sentiment analysis (ABSA) may provide more detailed information than general sentiment analysis. It aims to extract aspects from reviews and predict their polarities. In this paper, we focus on aspect extraction sub-task. We propose three weakly- supervised systems based on contextual language models and topic modeling. We evaluate and compare our systems on SemEval- 2016 restaurant french benchmark. The experimental results reveal that our systems is quite competitive in aspect extraction from user reviews. We obtain 60.65% as F1 score with our best system. The latter outperforms the existing supervised ones. We deduct that weakly-supervised systems are efficient in terms of performance, time and human effort.