Exploring Sentiment Analysis on Social Media Texts

Authors

  • Najeeb Abdulazez Alabdulkarim Department of Computer Science, College of Computer and Information Science, Majmaah University, 11952, Saudi Arabia
  • Mohd Anul Haq Department of Computer Science, College of Computer and Information Science, Majmaah University, 11952, Saudi Arabia
  • Jayadev Gyani Department of Computer Science, College of Computer and Information Science, Majmaah University, 11952, Saudi Arabia
Volume: 14 | Issue: 3 | Pages: 14442-14450 | June 2024 | https://doi.org/10.48084/etasr.7238

Abstract

Sentiment analysis is a critical component in understanding customer opinions and reactions. This study explores the application of sentiment analysis using Python on the Amazon Fine Food Reviews dataset to classify customer reviews as positive or negative, enabling businesses to gain valuable insight into customer sentiments. This study used and compared the efficiency of Logistic Regression, Support Vector Machines, Random Forest, XGBoost, LSTM, and ALBERT. The comparison results showed that the LSTM and ALBERT classifiers stand out with remarkable accuracy (96%) and substantial support for positive and negative reviews. On the other hand, although the Random Forest classifier had similar accuracy (96%), it exhibited lower support for positive and negative sentiments.

Keywords:

LSTM, XGBOOST, sentiment analysis, classification, ALBERT, regression, SVM

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How to Cite

[1]
N. A. Alabdulkarim, M. A. Haq, and J. Gyani, “Exploring Sentiment Analysis on Social Media Texts”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14442–14450, Jun. 2024.

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