Online　communities　are　a　rapidly　growing　knowledge　repository　that　provides　scholarly　research,　technical　discussion,　and　social　interactivity.　This　abundance　of　online　information　increases　the　difficulty　of　keeping　up　with　new　developments　difficult　for　researchers　and　practitioners.　Thus,　we　introduced　a　novel　method　that　analyses　both　knowledge　and　social　sentiment　within　the　online　community　to　discover　the　topical　coverage　of　emerging　technology　and　trace　technological　trends.　The　method　utilizes　the　Weibull　distribution　and　Shannon　entropy　to　measure　and　link　social　sentiment　with　technological　topics.　Based　on　question-and-answer　and　social　sentiment　data　from　Zhihu,　which　is　an　online　question　and　answer　(Q&A)　community　with　high-profile　entrepreneurs　and　public　intellectuals,　we　built　an　undirected　weighting　network　and　measured　the　centrality　of　nodes　for　technology　identification.　An　empirical　study　on　artificial　intelligence　technology　trends　supported　by　expert　knowledge-based　evaluation　and　cognition　provides　sufficient　evidence　of　the　method＇s　ability　to　identify　technology.　We　found　that　the　social　sentiment　of　hot　technological　topics　presents　a　long-tailed　distribution　statistical　pattern.　High　similarity　between　the　topic　popularity　and　emerging　technology　development　trends　appears　in　the　online　community.　Finally,　we　discuss　the　findings　in　various　professional　fields　that　are　widely　applied　to　discover　and　track　hot　technological　topics.　©　2021