Sentiment Classification Performance Analysis Based on Glove Word Embedding

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Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
Volume:25 Issue:3
Sentiment Classification Performance Analysis Based on Glove Word Embedding

Sentiment Classification Performance Analysis Based on Glove Word Embedding

Authors : Yasin KIRELLİ, Şebnem ÖZDEMİR

Pages : 639-646

Doi:10.16984/saufenbilder.886583

View : 39 | Download : 11

Publication Date : 2021-06-30

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(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 pretrained, embedded and deterministic word embedding classification performance has analyzed by using Long Short Term Memory insert ignore into journalissuearticles values(LSTM);. The porposed LSTM based deep learning model has been tested on three different data sets and the results was evaluated.
Keywords : sentiment classification, word embedding, word weight, glove word embedding

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