This　study　addresses　the　problem　of　Chinese　microblog　opinion　retrieval,　which　aims　to　retrieve　opinionated　Chinese　microblog　posts　relevant　to　a　target　specified　by　a　user　query.　Existing　studies　have　shown　that　lexicon-based　approaches　employed　online　public　sentiment　resources　to　rank　sentimentwords　relying　on　the　document　features.　However,　this　approach　could　not　be　effectively　applied　to　microblogs　that　have　typical　user-generated　content　with　valuable　contextual　information:　＂user-user＂　interpersonal　interactions　and　＂user-post/comment＂　intrapersonal　interactions.　This　contextual　information　is　very　helpful　in　estimating　the　strength　of　sentiment　words　more　accurately.　In　this　study,　we　integrate　the　social　contextual　relationships　among　users,　posts/comments,　and　sentiment　words　into　a　mutual　reinforcement　model　and　propose　a　unified　three-layer　heterogeneous　graph,　on　which　a　random　walk　sentiment　word　weighting　algorithm　is　presented　to　measure　the　strength　of　opinion　of　the　sentiment　words.　Furthermore,　the　weights　of　sentiment　words　are　incorporated　into　a　lexicon-based　model　for　Chinese　microblog　opinion　retrieval.　Comparative　experiments　are　conducted　on　a　Chinese　microblog　corpus,　and　the　results　show　that　our　proposed　mutual　reinforcement　model　achieves　significant　improvement　over　previous　methods.