News comments provide a convenient way for people to express opinions and exchange ideas. Positive comments contribute to encouraging a harmonious discussion atmosphere within news media communities, while offensive or insulting comments may result in cyberbullying and personal psychological trauma, which has particular practical impacts in security related domain. The automatic generation of news comments with controllable attributes (e.g. sentiment) to assist users and news platform administrators is of great need. However, existing research for news comment generation has not address this issue yet. Existing methods for controllable text generation focus on token-level constraints, which are not applicable to control the sentence-level attributes for news comment generation. To address this challenging issue, in this paper, we propose an attribute level contrastive learning method for controllable news comment generation. To apply attribute level constraints on the generated text, our method considers the attributes of generated comments and pre-defined attributes as different views of the same attribute, and maximizes their similarity during the training process. We conduct experiments on two public available news comment datasets, and the experimental results show that our model achieves competitive performance in terms of both news comment generation quality and attribute controllability.