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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| Format: | Article |
| Language: | English |
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Sakarya University
2021-06-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
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| 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 |