- Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
- Vol: 25 Issue: 3
- Sentiment Classification Performance Analysis Based on Glove Word Embedding
Sentiment Classification Performance Analysis Based on Glove Word Embedding
Authors : Yasin Kirelli, Şebnem Özdemir
Pages : 639-646
Doi:10.16984/saufenbilder.886583
View : 18 | Download : 7
Publication Date : 2021-06-30
Article Type : Other
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 (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 (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