Sentiment Classification Performance Analysis Based on Glove Word Embedding

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 o...

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Main Authors: Yasin Kırelli, Şebnem Özdemir
Format: Article
Language:English
Published: Sakarya University 2021-06-01
Series:Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
Subjects:
Online Access:https://dergipark.org.tr/tr/download/article-file/1601149
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author Yasin Kırelli
Şebnem Özdemir
author_facet Yasin Kırelli
Şebnem Özdemir
author_sort Yasin Kırelli
collection DOAJ
description 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.
format Article
id doaj-art-7189f765c5ce465793a1a4201e89a0b3
institution Kabale University
issn 2147-835X
language English
publishDate 2021-06-01
publisher Sakarya University
record_format Article
series Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
spelling doaj-art-7189f765c5ce465793a1a4201e89a0b32024-12-23T08:08:39ZengSakarya UniversitySakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi2147-835X2021-06-0125363964610.16984/saufenbilder.88658328Sentiment Classification Performance Analysis Based on Glove Word EmbeddingYasin Kırelli0https://orcid.org/0000-0002-3605-8621Şebnem Özdemir1https://orcid.org/0000-0001-6668-6285İSTİNYE ÜNİVERSİTESİİSTİNYE ÜNİVERSİTESİRepresentation 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.https://dergipark.org.tr/tr/download/article-file/1601149sentiment classificationword embeddingword weightglove word embedding
spellingShingle Yasin Kırelli
Şebnem Özdemir
Sentiment Classification Performance Analysis Based on Glove Word Embedding
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
sentiment classification
word embedding
word weight
glove word embedding
title Sentiment Classification Performance Analysis Based on Glove Word Embedding
title_full Sentiment Classification Performance Analysis Based on Glove Word Embedding
title_fullStr Sentiment Classification Performance Analysis Based on Glove Word Embedding
title_full_unstemmed Sentiment Classification Performance Analysis Based on Glove Word Embedding
title_short Sentiment Classification Performance Analysis Based on Glove Word Embedding
title_sort sentiment classification performance analysis based on glove word embedding
topic sentiment classification
word embedding
word weight
glove word embedding
url https://dergipark.org.tr/tr/download/article-file/1601149
work_keys_str_mv AT yasinkırelli sentimentclassificationperformanceanalysisbasedonglovewordembedding
AT sebnemozdemir sentimentclassificationperformanceanalysisbasedonglovewordembedding