With　the　development　of　e-shopping,　a　list　of　similar　products　can　be　found　with　a　large　volume　of　valuable　customer　reviews　online.　However,　it　is　generally　difficult　to　compare　various　aspects　of　similar　products　effectively　by　understanding　all　relevant　online　opinions.　To　help　consumers,　in　this　study,　how　products　are　ranked　according　to　online　reviews　is　investigated.　Firstly,　an　SC-LDA　(Seed　Constraint-Latent　Dirichlet　Allocation)　model,　which　is　an　extension　of　the　classical　topic　model　LDA　(Latent　Dirichlet　Allocation),　is　proposed　to　extract　product　features.　The　must-link　and　cannot-link　seed　constraints　are　invited　to　estimate　the　probability　expansion/reduction　value.　They　help　to　affect　the　topic　allocation　by　additional　constraints　in　Gibbs　sampling　for　a　higher　accuracy　on　feature　extraction.　Secondly,　an　improved　convolutional　memory　neural　network　model　is　devised　to　analyze　the　sentiment　polarity.　It　takes　the　advantages　of　CNN　(convolutional　neural　network)　and　Bi-LSTM　(bidirectional　Long　Short-Term　Memory)　and　performs　dynamic　pooling　in　CNN　to　prevent　the　loss　of　important　features.　Besides,　the　concept　of　group　satisfaction　degree　is　introduced,　which　makes　products　be　compared　according　to　the　Regret　Theory.　It　ranks　products　without　a　commonly　applied　reference　point　and　take　consumer　psychology　into　considerations.　Finally,　in　the　case　study,　an　illustrative　example　is　presented　to　evaluate　the　proposed　framework.　Categories　of　experiments　show　that　the　proposed　framework　provides　consumers　with　effective　purchase　suggestions.　Code　metadata:　Permanent　link　to　reproducible　Capsule:　https://doi.org/10.24433/CO.6445683.v1　and　https://d　oi.org/10.24433/CO.2658577.v1.