@article{MAKHILLJEAS202116119510,
    title = {Artificial Intelligence-Based Sentiment Analysis},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {16},
    number = {1},
    pages = {18-22},
    year = {2021},
    issn = {1816-949x},
    doi = {jeasci.2021.18.22},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2021.18.22},
    author = {Saleh,Aymen and},
    keywords = {BERT,latest technology,neural network,accuracy},
    abstract = {Bidirectional Encoder Representations from
Transformers (BERT) represents the latest technology of
pre-trained language models which have recently
advanced a wide range of natural language processing
tasks. This study aims to investigate how BERT can be
usefully applied in sentiment analysis tasks with fully
connected neural network. The proposed model is
developed using simple tips preventing it from
over-fitting and enabling it to be fine-tuned easily on such
down stream task. BERT performs much better as a
Strong text embedding Model. Using such procedures
successfully provide a better accuracy than the expensive
machine learning procedures.}
    }