Mining　online　reviews　has　become　an　important　means　of　identifying　consumer　behavior　and　the　innovation　direction　of　products.　However,　it　is　difficult　for　both　producers　and　consumers　to　effectively　analyze　and　extract　relevant　opinions　from　a　vast　number　of　online　reviews.　To　overcome　this　problem,　a　product　ranking　method　that　combines　feature-opinion　pairs　mining　and　interval-valued　Pythagorean　fuzzy　(IVPF)　sets　was　proposed　in　this　study.　First,　three　types　of　important　feature-opinion　pairs　were　clearly　defined　based　on　the　diversity　and　complexity　of　opinion　expression　forms　in　Chinese　ecommerce　reviews.　Two　deep　learning　models　were　then　designed　to　automatically　extract　the　feature-opinion　terms　and　match　them　into　pairs.　Afterwards,　sentiment　analysis　techniques　were　applied　to　identify　sentiment　orientation,　and　the　feature-opinion　pairs　were　clustered　into　groups　using　K-means　clustering　algorithm.　Meanwhile,　considering　the　confidence　level　based　on　the　number　of　online　reviews　on　different　products,　sentiment　value　was　transformed　into　interval-value　from,　including　interval　membership　and　non-membership.　As　the　sum　of　the　converted　interval　membership　and　non-membership　was　greater　than　1　and　their　quadratic　sum　was　less　than　1,　IVPF　set　was　introduced　to　represent　the　interval-valued　sentiment.　Furthermore,　based　on　the　interrelationship　between　product　attributes,　we　proposed　an　IVPF　weighted　Heronian　mean　operator　to　aggregate　the　attribute　information.　Product　ranking　was　then　achieved　based　on　the　operator　and　operations　under　the　IVPF　information.　Finally,　a　case　study　was　used　to　verify　the　feasibility　of　the　proposed　method,　and　comparisons　and　sensitivity　analysis　were　performed　to　demonstrate　the　superiority　of　our　method.　(C)　2020　Elsevier　B.V.　All　rights　reserved.