Sentiment Analysis through Transfer Learning for Turkish Language
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Sentiment Analysis (SA) has received much attention in recent years. In this paper, we proposed a model based on the transfer learning technique to address SA problem. First, we utilize word embeddings that are trained on 322K documents from Turkish Wikipedia. The model employs a regular Long Short-Term Memory (LSTM) with dropout. Secondly, we fine-tuned the pre-trained language model on two different target datasets (restaurant and product reviews) independently. Finally, the LSTM is trained to classify reviews according to positive and negative sentiments and its associated performance is assessed. This study is also considered to be the important attempt that uses transfer learning by applying a fine-tuning technique and deep learning architecture to address SA problem for Turkish Language. © 2019 IEEE.
Açıklama
Bulgarian National Science Fund;Bulgarian Section
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 -- 3 July 2019 through 5 July 2019 -- -- 150190
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 -- 3 July 2019 through 5 July 2019 -- -- 150190
Anahtar Kelimeler
Deep Learning, Lstm, Transfer Learning, Deep Learning, Intelligent Systems, Sentiment Analysis, Language Model, Learning Architectures, Lstm, Model-Based Opc, Negative Sentiments, Product Reviews, Transfer Learning, Turkish Language, Long Short-Term Memory
Kaynak
IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
