For　software　evolution,　user　feedback　has　become　a　meaningful　way　to　improve　applications.　Recent　studies　show　a　significant　increase　in　analyzing　end-user　feedback　from　various　social　media　platforms　for　software　evolution.　However,　less　attention　has　been　given　to　the　end-user　feedback　for　low-rating　software　applications.　Also,　such　approaches　are　developed　mainly　on　the　understanding　of　human　annotators　who　might　have　subconsciously　tried　for　a　second　guess,　questioning　the　validity　of　the　methods.　For　this　purpose,　we　proposed　an　approach　that　analyzes　end-user　feedback　for　low-rating　applications　to　identify　the　end-user　opinion　types　associated　with　negative　reviews　(an　issue　or　bug).　Also,　we　utilized　Generative　Artificial　Intelligence　(AI),　i.e.,　ChatGPT,　as　an　annotator　and　negotiator　when　preparing　a　truth　set　for　the　deep　learning　(DL)　classifiers　to　identify　end-user　emotion.　For　the　proposed　approach,　we　first　used　the　ChatGPT　Application　Programming　Interface　(API)　to　identify　negative　end-user　feedback　by　processing　71853　reviews　collected　from　45　apps　in　the　Amazon　store.　Next,　a　novel　grounded　theory　is　developed　by　manually　processing　end-user　negative　feedback　to　identify　frequently　associated　emotion　types,　including　anger,　confusion,　disgust,　distrust,　disappointment,　fear,　frustration,　and　sadness.　Next,　two　datasets　were　developed,　one　with　human　annotators　using　a　content　analysis　approach　and　the　other　using　ChatGPT　API　with　the　identified　emotion　types.　Next,　another　round　is　conducted　with　ChatGPT　to　negotiate　over　the　conflicts　with　the　human-annotated　dataset,　resulting　in　a　conflict-free　emotion　detection　dataset.　Finally,　various　DL　classifiers,　including　LSTM,　BILSTM,　CNN,　RNN,　GRU,　BiGRU　and　BiRNN,　are　employed　to　identify　their　efficacy　in　detecting　end-users　emotions　by　preprocessing　the　input　data,　applying　feature　engineering,　balancing　the　data　set,　and　then　training　and　testing　them　using　a　cross-validation　approach.　We　obtained　an　average　accuracy　of　94%,　94%,　93%,　92%,　91%,　91%,　and　85%,　with　LSTM,　BILSTM,　RNN,　CNN,　GRU,　BiGRU　and　BiRNN,　respectively,　showing　improved　results　with　the　truth　set　curated　with　human　and　ChatGPT.　Using　ChatGPT　as　an　annotator　and　negotiator　can　help　automate　and　validate　the　annotation　process,　resulting　in　better　DL　performances.　©　2024　Elsevier　Ltd