@inproceedings{b9e654e2c1de4b0b8621aa26b52d9dfd,
title = "Sentiment Analysis using DistilBERT",
abstract = "Transformers is an architecture that performs well in NLP task. To understand and improve its performance on sentiment analysis, DistilBERT is employed as the base model. Sentiment analysis is a process that extracts subjective information from textual data and categorizes them into different classes. The classification classes may include polarity (positive, neutral, negative) or emotions (happy, sad, angry). In addition, multiple techniques such as fine tuning, regularization and hyperparameter tuning are applied to improve the performance of the model. The proposed solution acquired an accuracy score of 85.41% on Internet Movie Database (IMDB) dataset and 86.59% on Customer Reviews (CR) dataset.",
keywords = "Deep Learning, DistilBERT, Sentiment Analysis, Transformers",
author = "Ng, {Song Yi} and Lim, {Kian Ming} and Lee, {Chin Poo} and Lim, {Jit Yan}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th IEEE Conference on Systems, Process and Control, ICSPC 2023 ; Conference date: 16-12-2023",
year = "2023",
doi = "10.1109/ICSPC59664.2023.10420272",
language = "English",
series = "2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "84--89",
booktitle = "2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings",
address = "United States",
}