App　stores　allow　users　to　search,　download,　and　purchase　software　applications　to　accomplish　daily　tasks.　Also,　they　enable　crowd-users　to　submit　textual　feedback　or　star　ratings　to　the　downloaded　software　apps　based　on　their　satisfaction.　Recently,　crowd-user　feedback　contains　critical　information　for　software　developers,　including　new　features,　issues,　non-functional　requirements,　etc.　Previously,　identifying　software　bugs　in　low-star　software　applications　was　ignored　in　the　literature.　For　this　purpose,　we　proposed　a　natural　language　processing-based　(NLP)　approach　to　recover　frequently　occurring　software　issues　in　the　Amazon　Software　App　(ASA)　store.　The　proposed　approach　identified　prevalent　issues　using　NLP　part-of-speech　(POS)　analytics.　Also,　to　better　understand　the　implications　of　these　issues　on　end-user　satisfaction,　different　machine　learning　(ML)　algorithms　are　used　to　identify　crowd-user　emotions　such　as　anger,　fear,　sadness,　and　disgust　with　the　identified　issues.　To　this　end,　we　shortlisted　45　software　apps　with　comparatively　low　ratings　from　the　ASA　Store.　We　investigated　how　crowd-users　reported　their　grudges　and　opinions　against　the　software　applications　using　the　grounded　theory　&　content　analysis　approaches　and　prepared　a　grounded　truth　for　the　ML　experiments.　ML　algorithms,　such　as　MNB,　LR,　RF,　MLP,　KNN,　AdaBoost,　and　Voting　Classifier,　are　used　to　identify　the　associated　emotions　with　each　captured　issue　by　processing　the　annotated　end-user　data　set.　We　obtained　satisfactory　classification　results,　with　MLP　and　RF　classifiers　having　82%　and　80%　average　accuracies,　respectively.　Furthermore,　the　ROC　curves　for　better-performing　ML　classifiers　are　plotted　to　identify　the　best-performing　under　or　oversampling　classifier　to　be　selected　as　the　final　best　classifier.　Based　on　our　knowledge,　the　proposed　approach　is　considered　the　first　step　in　identifying　frequently　occurring　issues　and　corresponding　end-user　emotions　for　low-ranked　software　applications.　The　software　vendors　can　utilize　the　proposed　approach　to　improve　the　performance　of　low-ranked　software　apps　by　incorporating　it　into　the　software　evolution　process　promptly.　Authors