Exploring Sentiment Analysis on Social Media Texts
Received: 12 March 2024 | Revised: 24 March 2024 | Accepted: 2 April 2024 2024 | Online: 1 June 2024
Corresponding author: Mohd Anul Haq
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, SVMDownloads
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Copyright (c) 2024 Najeeb Abdulazez Alabdulkarim, Mohd Anul Haq, Jayadev Gyani
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