@article{MAKHILLJEAS2018131716764,
    title = {A Large-Scale Arabic Sentiment Corpus Construction Using Online News Media},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {17},
    pages = {7329-7340},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.7329.7340},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.7329.7340},
    author = {Ahmed and},
    keywords = {Sentiment analysis,large-scale corpus,bigdata,machine learning,ensemble learning,experiments},
    abstract = {Within computer-based technologies, the usage of collected data and its size are continuously on
a rise. This continuously growing big data processing and computational requirements introduce new
challenges, especially for Natural Language Processing (NLP) applications. One of these challenges is
maintaining massive information-rich linguistic resources which are fit with the requirements of the big data
handling, processing and analysis for NLP applications such as large-scale text corpus. In this research we
present a large-scale sentiment corpus for the Arabic language called GLASC which is built using online news
articles and metadata shared by the big data resource GDELT. Our GLASC corpus consists of a total number
of 620,082 news article which are organized in categories (Positive, negative and neutral). Besides that, each
news article within our corpus has a sentiment rating score in the range between-1 and 1. We have also carried
out some experiments on our corpus, using machine learning algorithms to generate a sentiment classifier for
document-level Arabic sentiment analyses. For training the sentiment classifier we generated different datasets
from our corpus using different feature extraction and feature weighting method. We performed a comparative
study, involving testing a wide range of classifiers that commonly used for sentiment analysis task and in
addition we investigated several types of ensemble learning methods to verify its effect on improving the
classification performance of sentiment analysis by using different comprehensive empirical experiments.}
    }