Utilization Of Principal Component Analysis To Improve Emotion Classification Performance In Text Using Artificial Neural Networks

Emotions, being transient and variable, differ across locations, times, and individuals. Automatic emotion identification holds significant importance across various domains, such as education and business. In education, emotional analysis contributes to intelligent electronic learning environments, while in business, it aids in assessing customer satisfaction with products. This study advocates the application of Principal Component Analysis (PCA) to enhance the performance of text emotion classification using the Artificial Neural Network (ANN) method. PCA, a pattern identification method, reduces text dimensions, improving the classification process by determining word similarities. PCA offers the advantage of dimension reduction without compromising information integrity. The classification approach involves two stages: one after PCA dimension reduction and the other without PCA post TF-IDF stage. The study's conclusive findings, incorporating PCA in ANN classification, demonstrated a notable increase in recall for the happy class, reaching 0.92 compared to the pre-PCA score of 0.91. Furthermore, precision in the sadness class improved to 0.90, surpassing the pre-PCA precision of 0.80. This affirms the efficacy of integrating PCA in enhancing the accuracy and performance of emotion classification in text analysis.