In　today＇s　digital　world,　app　stores　have　become　an　essential　part　of　software　distribution,　providing　customers　with　a　wide　range　of　applications　and　opportunities　for　software　developers　to　showcase　their　work.　This　study　elaborates　on　the　importance　of　end-user　feedback　for　software　evolution.　However,　in　the　literature,　more　emphasis　has　been　given　to　high-rating　&　popular　software　apps　while　ignoring　comparatively　low-rating　apps.　Therefore,　the　proposed　approach　focuses　on　end-user　reviews　collected　from　64　low-rated　apps　representing　14　categories　in　the　Amazon　App　Store.　We　critically　analyze　feedback　from　low-rating　apps　and　developed　a　grounded　theory　to　identify　various　concepts　important　for　software　evolution　and　improving　its　quality　including　user　interface　(UI)　and　user　experience　(UX),　functionality　and　features,　compatibility　and　device-specific,　performance　and　stability,　customer　support　and　responsiveness　and　security　and　privacy　issues.　Then,　using　a　grounded　theory　and　content　analysis　approach,　a　novel　research　dataset　is　curated　to　evaluate　the　performance　of　baseline　machine　learning　(ML),　and　state-of-the-art　deep　learning　(DL)　algorithms　in　automatically　classifying　end-user　feedback　into　frequently　occurring　issues.　Various　natural　language　processing　and　feature　engineering　techniques　are　utilized　for　improving　and　optimizing　the　performance　of　ML　and　DL　classifiers.　Also,　an　experimental　study　comparing　various　ML　and　DL　algorithms,　including　multinomial　naive　Bayes　(MNB),　logistic　regression　(LR),　random　forest　(RF),　multi-layer　perception　(MLP),　k-nearest　neighbors　(KNN),　AdaBoost,　Voting,　convolutional　neural　network　(CNN),　long　short-term　memory　(LSTM),　bidirectional　long　short　term　memory　(BiLSTM),　gated　recurrent　unit　(GRU),　bidirectional　gated　recurrent　unit　(BiGRU),　and　recurrent　neural　network　(RNN)　classifiers,　achieved　satisfactory　results　in　classifying　end-user　feedback　to　commonly　occurring　issues.　Whereas,　MLP,　RF,　BiGRU,　GRU,　CNN,　LSTM,　and　Classifiers　achieved　average　accuracies　of　94%,　94%,　92%,　91%,　90%,　89%,　and　89%,　respectively.　We　employed　the　SHAP　approach　to　identify　the　critical　features　associated　with　each　issue　type　to　enhance　the　improvement　in　low-rated　apps　and　opens　up　new　avenues　for　developers　to　improve　software　quality　based　on　user　feedback.