Empirical Evaluation of Word Representations on Arabic Sentiment Analysis
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER-VERLAG BERLIN
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Sentiment analysis is the Natural Language Processing (NLP) task that aims to classify text to different classes such as positive, negative or neutral. In this paper, we focus on sentiment analysis for Arabic language. Most of the previous works use machine learning techniques combined with hand engineering features to do Arabic sentiment analysis (ASA). More recently, Deep Neural Networks (DNNs) were widely used for this task especially for English languages. In this work, we developed a system called CNN-ASAWR where we investigate the use of Convolutional Neural Networks (CNNs) for ASA on 2 datasets: ASTD and SemEval 2017 datasets. We explore the importance of various unsupervised word representations learned from unannotated corpora. Experimental results showed that we were able to outperform the previous state-of-the-art systems on the datasets without using any kind of hand engineering features.
Açıklama
6th International Conference on Arabic Language Processing (ICALP) -- OCT 11-12, 2017 -- Fez, MOROCCO
Anahtar Kelimeler
Arabic language, Arabic sentiment analysis, Convolutional neural networks, Pretrained word representations
Kaynak
ARABIC LANGUAGE PROCESSING: FROM THEORY TO PRACTICE
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
782
