TY - GEN
T1 - Sentiment Analysis Using Learning-based Approaches: A Comparative Study
AU - Ng, Jing Xiang
AU - Lim, Kian Ming
AU - Lee, Chin Poo
AU - Lim, Qi Zhi
AU - Ooi, Eric Khang Heng
AU - Loh, Nicole Kai Ning
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Sentiment analysis, which involves analyzing text data and using language computation to extract valuable information, is a significant focus in Natural Language Processing (NLP). It is widely used in various applications such as product review analysis, customer feedback analysis, and social media monitoring. This research investigates the performance of different machine learning and deep learning models for sentiment analysis on a dataset of customer reviews from an e-commerce platform. A total of eight approaches have been presented in this study including LightGBM, SVM, KNN with bagging, MultinomialNB, DNN, LSTM, BERT, and RoBERTa. The performance for all the proposed models was compared using four evaluation metrics: accuracy, precision, recall and F1-score. The experimental results indicate that the SVM model has outperformed all the other methods with a testing accuracy of 73.98%. The F1-score, precision and recall are also the highest at 0.71, 0.72 and 0.70 respectively. This study contributes to the sentiment analysis literature by demonstrating the effectiveness of different models for sentiment analysis on customer reviews datasets.
AB - Sentiment analysis, which involves analyzing text data and using language computation to extract valuable information, is a significant focus in Natural Language Processing (NLP). It is widely used in various applications such as product review analysis, customer feedback analysis, and social media monitoring. This research investigates the performance of different machine learning and deep learning models for sentiment analysis on a dataset of customer reviews from an e-commerce platform. A total of eight approaches have been presented in this study including LightGBM, SVM, KNN with bagging, MultinomialNB, DNN, LSTM, BERT, and RoBERTa. The performance for all the proposed models was compared using four evaluation metrics: accuracy, precision, recall and F1-score. The experimental results indicate that the SVM model has outperformed all the other methods with a testing accuracy of 73.98%. The F1-score, precision and recall are also the highest at 0.71, 0.72 and 0.70 respectively. This study contributes to the sentiment analysis literature by demonstrating the effectiveness of different models for sentiment analysis on customer reviews datasets.
KW - BERT
KW - LightGBM
KW - RoBERTa
KW - Sentiment Analysis
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85174413554&partnerID=8YFLogxK
U2 - 10.1109/ICoICT58202.2023.10262604
DO - 10.1109/ICoICT58202.2023.10262604
M3 - Conference Proceeding
AN - SCOPUS:85174413554
T3 - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
SP - 469
EP - 474
BT - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
T2 - 11th International Conference on Information and Communication Technology, ICoICT 2023
Y2 - 23 August 2023 through 24 August 2023
ER -