Ensemble Classification Approach for Sarcasm Detection.

Area of Science:

• Cognitive Science
• Natural Language Processing
• Machine Learning

Background:

• Cognitive science analyzes the human brain using data mining techniques.
• Large datasets are crucial for extracting authenticated information.
• Sarcasm detection in text is a complex challenge within natural language processing.

Purpose of the Study:

• To develop and evaluate an effective scheme for detecting sarcasm in text data.
• To explore the efficacy of ensemble classification methods for sarcasm detection.
• To compare the performance of different ensemble models.

Main Methods:

• Utilized Principal Component Analysis (PCA) and K-means clustering algorithms.
• Designed four ensemble classifiers: SKD (SVM, KNN, Decision Tree), SLD (SVM, Logistic Regression, Decision Tree), MLD (MLP, Logistic Regression, Decision Tree), and SLM (MLP, Logistic Regression, SVM).
• Implemented the models in Python and tested on five diverse datasets.

Main Results:

• The proposed ensemble models demonstrated performance in sarcasm detection across various datasets.
• Comparative analysis of the four ensemble classifiers was conducted using multiple performance metrics.
• The study provides insights into the effectiveness of combining different machine learning algorithms for this task.

Conclusions:

• Ensemble classification approaches show promise for accurate sarcasm detection.
• The specific combinations of algorithms in SKD, SLD, MLD, and SLM offer distinct performance characteristics.
• Further research can refine these models for enhanced natural language understanding.