@inproceedings{c1742776ad5a4e71a4e5a22cc98b588f,
title = "Arabic Sentiment Classification: A Hybrid Approach",
abstract = "This paper proposes a sentiment analysis approach for the Arabic language that combines lexicon based and corpus based techniques. The main idea of this approach is to represent the review for the corpus-based approach in the same way it is seen in lexicon-based approach, through replacing the polarity words with their corresponding label Positive 'POS' or Negative 'NEG' in the lexicon, this way the terms that are important but rare can be taken into consideration by the classifier. A comprehensive comparison is conducted using different classifiers, and experimental results showed that the proposed hybrid approach outperforms the corpus-based approach and the highest accuracy reached 96.34\% using random forest classifier with 6-fold cross validation.",
keywords = "Arabic sentiment analysis, Natural Language Processing, data science, lexicon and corpus based, supervised learning",
author = "Mariam Biltawi and Ghazi Al-Naymat and Sara Tedmori",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on New Trends in Computing Sciences, ICTCS 2017 ; Conference date: 11-10-2017 Through 13-10-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/ICTCS.2017.24",
language = "English",
series = "Proceedings - 2017 International Conference on New Trends in Computing Sciences, ICTCS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "104--108",
editor = "Arafat Awajan and Adnan Shaout",
booktitle = "Proceedings - 2017 International Conference on New Trends in Computing Sciences, ICTCS 2017",
address = "United States",
}