Sentiment classification performance analysis based on glove word embedding
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Dosyalar
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
DergiPark
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Representation 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 evaluated
Açıklama
Anahtar Kelimeler
Sentiment Classification, Word Embedding, Word Weight, Glove Word Embedding
Kaynak
Sakarya University Journal of Science
WoS Q Değeri
Scopus Q Değeri
Cilt
25
Sayı
3
Künye
Kırelli, 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.886583
