Aspect-based　sentiment　classification　(ABSC)　aims　to　determine　the　sentiment　polarity　toward　a　specific　aspect.　In　order　to　finish　this　task,　it　is　difficult　to　match　a　specific　aspect　with　its　opinion　words　since　there　are　usually　multiple　aspects　with　different　opinion　words　in　a　sentence.　Many　efforts　have　been　made　to　address　this　problem,　such　as　graph　neural　networks　and　attention　mechanism,　however　come　at　the　cost　of　the　introduced　extraneous　noise,　leading　to　mismatches　of　the　aspect　with　its　opinion　words.　In　this　paper,　we　propose　a　Mutual　Information-based　ABSC　model,　called　MatchMaker,　which　introduces　Mutual　Information　estimation　to　strengthen　the　correlations　between　a　specific　aspect　and　its　opinion　words　without　introducing　any　extraneous　noise,　thus　significantly　improving　the　accuracy　when　determining　the　sentiment　polarity　toward　a　specific　aspect.　Experimental　results　show　that　our　method　with　Mutual　Information　is　effective.　For　example,　MatchMaker　obtains　a　significant　improvement　of　accuracy　over　ASGCN　model　by　3.1%　on　the　Rest14.　©　2021,　Springer　Nature　Switzerland　AG.