Explainability　has　recently　been　recognized　as　an　increasingly　important　quality　requirement　for　machine　learning　systems.　Various　methods　have　been　proposed　by　machine　learning　researchers　to　explain　the　results　of　machine　learning　techniques.　However,　analyzing　and　operationalizing　such　explainability　requirements　is　knowledge-intensive　and　timeconsuming.　This　paper　proposes　an　explainability　requirements　analysis　framework　using　contextual　goal　models,　aiming　at　systematically　and　automatically　deriving　appropriate　explainability　methods.　Specifically,　we　comprehensively　survey　and　analyze　existing　explainability　methods,　associating　them　with　explainability　requirements　and　emphasizing　the　context　for　applying　them.　In　such　a　way,　we　can　automatically　operationalize　explainability　requirements　into　concrete　explainability　methods.　We　conducted　a　case　study　with　ten　participants　to　evaluate　our　proposal.　The　results　illustrate　the　framework＇s　usability　for　satisfying　the　explainability　requirements　of　machine　learning　systems.