Co-DLGAX: A Hybrid Ensemble Framework for Emotion Detection via NLP-Driven Sentiment Analysis

In social media, many queries of text mining have exploded. Today, Text analysis is an increasing the utilization in popularity especially online. This trend should continue. Social media networks generate a lot of text data, allowing users to freely comment. The rise of social media contributes to this. Many commercial applications require comment analysis. NLP Sentiment Analysis (SA) is essential for identifying feelings in reviews and comments. This paper explains how to use the TF-IDF vectorizer to create a feature extraction ensemble machine learning model. We want to propose the Co-DLGAX (Combination of Decision tree, Logistic Regression, Guassion NB, AdaBoost, and XGBoost). SA usually predicts user comment emotions. The principal goals of this proposal fall into three areas. We collect the data set and classify the data set as good or bad. Our process started here. The second step involved an exhaustive review of six classification systems. We create the combined classifier named as Co-DLGAX to provide better outcomes than other models such as Decision Tree, Logistic Regression, Gaussian NB, AdaBoost, and XGBoost with the Borderland Emotion Dataset.