Automatic Song Lyric Generation and Classification with Long Short-Term Networks Lyric classification and generation are trending in topics in the machine learning community. Long Short-Term Networks (LSTMs) are effective tools for classifying and generating text. We explored their effectiveness in the generation and classification of lyrical data and proposed methods of evaluating their accuracy. We found that LSTM networks with dropout layers were effective at lyric classification. We also found that Word embedding LSTM networks were extremely effective at lyric generation.autTallapragada, AmitthsBen Amor, HenidgcCaviedes, JorgectbComputer Science and Engineering ProgramctbComputer Science and Engineering ProgramctbBarrett, The Honors Collegeenghttps://hdl.handle.net/2286/R.I.521836 pages115539220031628716197132995atallaprIn Copyright2019-05TextMachine LearningNeural NetworksLSTMSong GenerationSong Classification