Kırelli, YasinÖzdemir, Şebnem2021-07-052021-07-052021Kırelli, Y , Özdemir, Ş . (2021). Sentiment Classification Performance Analysis Based on Glove Word Embedding . Sakarya University Journal of Science , 25 (3) , 639-646 . DOI: 10.16984/saufenbilder.8865832147-835Xhttps://doi.org/10.16984/saufenbilder.886583https://hdl.handle.net/20.500.12713/1880Representation of words in mathematical expressions is an essential issue in natural language processing. In this study, data sets in different categories are classified as positive or negative according to their content. Using the Glove (Global Vector for Word Representation) method, which is one of the word embedding methods, the effect of the vector set based on the word similarities previously calculated on the classification performance has been analyzed. In this study, the effect of pre-trained, embedded and deterministic word embedding classification performance has analyzed by using Long Short-Term Memory (LSTM). The proposed LSTM based deep learning model has been tested on three different data sets and the results have been evaluatedeninfo:eu-repo/semantics/openAccessSentiment ClassificationWord EmbeddingWord WeightGlove Word EmbeddingArticle25363964610.16984/saufenbilder.886583469602