Online　reviews　are　a　valuable　tool　for　Multi　-Criteria　Decision　Making　(MCDM)　because　they　provide　insightful　information　on　several　aspects.　However,　not　all　reviews　yield　complete　information　on　all　the　necessary　criteria　for　the　MCDM　problem.　In　practice,　users　might　not　effectively　analyze　all　of　them　due　to　a　lack　of　knowledge　or　time　constraints.　Despite　this　shortcoming,　these　online　reviews　still　offer　useful　information　that　would　be　lost　if　neglected.　Therefore,　this　paper　proposes　a　framework　that　offers　a　new　approach　to　using　online　reviews　in　MCDM　and　effectively　addresses　missing　information　on　a　given　criterion　or　criteria.　This　new　approach　first　establishes　an　information　processing　mechanism　to　transform　online　reviews　into　Probabilistic　Linguistic　Term　Sets　(PLTSs)　with　interval　uncertainty.　Afterward,　a　weight　determination　model　is　defined　to　calculate　the　criteria　weights,　which　are　given　as　interval　values　to　reflect　the　uncertainty　of　the　criteria　and　consider　information　from　online　reviews　to　better　capture　the　opinions　of　reviewers.　Next,　the　Evidential　Reasoning　(ER)　algorithm　is　extended　to　the　PLTSs　with　interval　uncertainty　environment　to　fuse　the　evaluation　information　and　generate　the　interval-valued　expected　utilities　of　the　alternatives　by　considering　the　uncertainty　related　to　the　missing　information.　Finally,　a　case　study　is　conducted　to　illustrate　the　implementation　process　of　the　proposed　framework,　and　a　comparative　analysis　is　carried　out　to　evaluate　its　performance.