Information　is　a　key　factor　that　influences　the　performance　of　decision　makers.　With　the　explosive　proliferation　of　Web　2.0,　the　volume　of　online　textual　reviews　has　been　sharply　increasing.　However,　how　to　use　this　type　of　unstructured　data　and　utilize　the　valuable　information　hidden　behind　are　still　problems　to　be　solved.　This　study　aims　to　provide　a　multi-attributes　decision　analysis　(MADA)　framework　based　on　incomplete　online　textual　reviews　to　aid　in　decision　making.　First,　online　textual　reviews　are　obtained　by　data　crawling.　Attributes　information　is　determined　by　textual　analysis　and　the　attitudes　of　assessors　toward　each　attribute　is　discriminated　by　the　sentiment　analysis.　Then,　some　new　rules　are　developed　in　encoding　incomplete　online　textual　reviews　into　interval-valued　linguistic　distribution　assessment　(ILDA)　to　better　characterize　the　evaluators’　attitudes.　Next,　evidential　reasoning　(ER)　algorithm　is　extended　to　the　ILDA　environment　to　combine　the　information　with　multiple　attributes,　and　the　utility　interval　of　each　alternative　is　constructed　by　solving　a　pair　of　nonlinear　optimization　models.　Given　that　the　interval　data　cannot　be　directly　compared,　an　enhanced　minimax　regret　approach　is　proposed　to　compare　and　rank　them.　Finally,　a　real　case　study　about　online　commodity　evaluation　is　examined　to　show　the　implementation　process　of　the　proposed　framework,　and　a　discussion　is　also　conducted　to　systematically　analyze　its　superiority.　©　2021　Elsevier　Inc.