/Sarcasm-Detection-Application

"Detect sarcasm effortlessly! This Python app uses NLP and ML to analyze text sentiment, distinguishing sarcastic tones. With a user-friendly interface, input any text for real-time sarcasm identification. Achieve accurate results through advanced sentiment analysis techniques and trained models."

Primary LanguagePythonMIT LicenseMIT

Sarcasm-Detection-Application

The Sarcasm Detection Application is a Python-based tool designed to identify and analyze sarcasm within textual content such as headlines and tweets. Leveraging advanced NLP techniques and Machine Learning algorithms, it determines whether text carries a sarcastic tone.

Key Features:

  • Cutting-edge Analysis: Utilizes TextBlob and SentiWordNet libraries for sentiment analysis to identify sarcastic expressions through polarity analysis.
  • Machine Learning Models: Employs SVM classifiers trained on proposed features and reduced N-gram features obtained via PCA for enhanced accuracy.
  • User-Friendly Interface: Offers an intuitive PyQt5-based interface for instant sarcasm detection and real-time feedback.

How It Works:

Processes input text by analyzing sentiment polarity at sentence and word levels to accurately detect sarcasm.

Usage:

Setup: Requires Python 3.x and installation of necessary libraries (textblob, nltk, scikit-learn, PyQt5). Running the Application: Users input tweets or text within the provided interface, initiating the sarcasm detection process with immediate results.

Dataset & Metrics:

Utilizes a preloaded dataset of headlines (Sarcasm_Headlines_Dataset.json) to assess accuracy, precision, recall, and F1 score metrics, ensuring model reliability and performance validation.

The Sarcasm Detection Application is a valuable tool for deciphering subtle sentiment nuances within text, allowing users to discern sarcasm and comprehend deeper sentiments conveyed in textual data.