Code comment is quite important to help developer review and comprehend source code. Strategic comment decision is desired to cover core code snippets of software system without incurring unintended trivial comments. However, in current practice, there is a lack of rigorous specifications for developers to make their comment decisions. Commenting has become an important yet tough decision which mostly depends on the personal experience of developers. To reduce the effort on making comment decisions, this paper investigates a unified commenting regulation from a large number of commenting instances. A method, CommentAdviser, is proposed to guide developers in placing comments in source code. Since making comment is closely related to the context information of source code themselves, the method identifies this important factor for determining where to comment and extract them as structural context feature and semantic context feature. Next, machine learning techniques are applied to identify the possible commenting locations in source code. CommentAdviser is evaluated on 10 data sets from GitHub. The experimental results, as well as a user study, demonstrate the feasibility and effectiveness of CommentAdviser.