Sentiment Analysis with Five Emotion Categories on YouTube Using Classification Methods
Year: 2025
Type: conference-paper
Abstract: In this study, sentiment analysis was performed by creating a dataset of YouTube comments categorized into five emotion classes. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for feature selection, and classification was conducted using machine learning (Logistic Regression, Random Forest) and deep learning (LSTM, CNN, BiLSTM) algorithms. The highest performance was achieved with the LSTM model on PCA-processed data and with the Logistic Regression model on LDA-processed data, reaching 88% accuracy. This study contributes to the literature with its original dataset and a comparative analysis of the effects of PCA and LDA.
Authors: Gülay Çiçek, Nazlı Buldağ, Elif Aydın
Institution: Beykent University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413462467