The　intelligent　strategy　of　the　new　energy　vehicle　(NEV)　industry　has　triggered　the　rapid　prevalence　of　in-vehicle　anthropomorphic　artificial　intelligence　(AI)　assistants.　There　is　still　a　lack　of　clarity　regarding　NEV　users＇　attitudes　toward　this　cutting-edge　technology　and　whether　they　receive　a　satisfactory　intelligent　service　experience.　To　circumvent　potential　emerging　technology　resistance,　in　this　article,　we　utilize　text　analysis　techniques　for　the　identification　of　AI　interaction　emotions,　love　and　disgust　(enablers　and　inhibitors)　with　significant　influence　on　user　satisfaction,　and　validates　the　improving　role　of　multimodality　on　the　effectiveness　of　anthropomorphic　interaction.　In　addition,　this　study　innovatively　constructs　a　multidimensional　corpus　of　modality　x　emotion,　using　structural　topic　modeling　to　uncover　the　constituent　elements　and　real-time　changes　of　love　and　disgust　emotions　in　different　modalities,　from　which　development　opportunities　and　improvement　directions　for　AI　anthropomorphic　interaction　technologies　are　identified.　The　findings　provide　new　insights　into　the　application　of　emotion　analysis　methods　to　improve　users＇　intelligent　service　experience　and　provide　a　realistic　reference　for　mitigating　emerging　technology　resistance　in　the　NEV　industry.