Microblogging　services,　such　as　Twitter,　have　become　popular　for　people　to　share　their　opinions　towards　a　broad　range　of　topics.　It　is　a　great　challenge　to　get　an　overview　of　some　important　topics　by　reading　all　tweets　every　day.　Previous　researches　such　as　opinion　detection　and　opinion　summarization　have　been　studied　for　this　problem.　However,　these　works　mainly　focus　on　the　content　of　text　without　taking　the　quality　of　short　text　and　features　of　social　media　into　consideration.　In　this　paper,　we　propose　a　heterogeneous　graph　model　for　users’　opinion　detection　on　microblog.　We　first　extract　keywords　of　topics.　Then,　a　three-level　microblog　graph　is　constructed　by　combining　user　influence,　word　importance,　post　significance,　and　topic　periodicity.　Microblog　posts　are　ranked　from　different　topics　by　using　the　random　walk　algorithm.　Experimental　results　on　real　a　dataset　validate　the　effectiveness　of　our　approach.　In　comparison　with　baseline　approaches,　the　proposed　method　achieves　8%　improvement.　©　Springer-Verlag　Berlin　Heidelberg　2014.