G.O.D. Framework

Introduction

The ai_emotion_analyzer.py module is a core component of the G.O.D Framework, specializing in detecting and analyzing emotional contexts from textual, visual, or audio inputs. Incorporating state-of-the-art sentiment analysis and emotional intelligence models, this module enables downstream AI tools to dynamically adapt based on user emotions.

This tool is designed for applications in human-computer interaction, content moderation, customer sentiment tracking, and AI personalization.

Purpose

• Emotion Detection: Identify human emotions (e.g., joy, anger, sadness) from textual or multimodal data inputs.
• Context-Specific Sentiment Analysis: Classify positive, negative, or neutral sentiments.
• Adaptive AI Interaction: Help adapt conversational agents based on user emotional states.
• Human-AI Emotional Insights: Provide interpretable emotional analysis for decision-making in AI-driven systems.
• User Experience Optimization: Track user sentiment to improve engagement and satisfaction.

Key Features

• Sentiment Analyzer: Employs transformer-based models (e.g., BERT, RoBERTa) for text sentiment classification.
• Emotion Detection: Uses advanced deep learning techniques to classify emotions accurately.
• Multimodal Emotion Recognition: Provides tools for analyzing audio (speech tone) or images (facial expressions).
• Custom Sentiment Models: Facilitates the integration of custom-trained models for industry-specific emotional contexts.
• Real-Time Processing: Offers real-time analysis capabilities for conversational AI or live input streams.

Logic and Implementation

The ai_emotion_analyzer.py script integrates pre-trained sentiment and emotion classification models to infer emotions from input data. For textual inputs, the module uses transformers like BERT or RoBERTa. For visual and audio modalities, the system employs CNN-based facial recognition or speech emotion recognition models.

            from transformers import pipeline
            import librosa
            import numpy as np

            class EmotionAnalyzer:
                """
                Multi-modal Emotion Analyzer for Text, Audio, and Visual inputs.
                """

                def __init__(self, model="bert-base-uncased"):
                    """
                    Initialize the emotion analysis pipeline using a pretrained text sentiment model.
                    :param model: Pre-trained transformer model for text processing.
                    """
                    self.text_analyzer = pipeline("sentiment-analysis", model=model)
                    # Placeholder for future visual/audio processors
                    self.audio_model = None  # e.g., Pre-trained speech emotion model
                    self.visual_model = None  # e.g., CNN model for facial expressions

                def analyze_text(self, text):
                    """
                    Perform sentiment and emotion analysis for input text.
                    :param text: Input text or document.
                    :return: Sentiment label and confidence score.
                    """
                    result = self.text_analyzer(text)
                    return result

                def analyze_audio(self, audio_path):
                    """
                    Analyze emotional tone in audio data (e.g., speech).
                    :param audio_path: Path to the audio file.
                    :return: Emotion label and confidence score.
                    """
                    # Placeholder for speech emotion analysis (e.g., librosa + pre-trained model)
                    y, sr = librosa.load(audio_path)
                    mfccs = np.mean(librosa.feature.mfcc(y=y, sr=sr).T, axis=0)
                    # Forward pass through a custom speech emotion model
                    # For now, return placeholder
                    return {"emotion": "neutral", "confidence": 0.8}

            if __name__ == "__main__":
                analyzer = EmotionAnalyzer()
                text_result = analyzer.analyze_text("I am so happy to use this AI framework!")
                print("Text Analysis Result:", text_result)

                # Audio processing example
                # audio_result = analyzer.analyze_audio("path_to_audio.mp3")
                # print("Audio Analysis Result:", audio_result)
            

Dependencies

The module relies on the following key libraries:

• transformers: For text-based sentiment analysis using pre-trained models.
• librosa: For audio signal processing (speech emotion detection).
• numpy: Numerical computations for feature extraction.
• torch: Deep learning model inference (if custom emotion models are incorporated).

Usage

The ai_emotion_analyzer.py module provides straightforward APIs for processing text, audio, or multi-modal inputs:

1. Import and initialize the EmotionAnalyzer class.
2. Use analyze_text() for textual emotion/sentiment analysis.
3. Integrate analyze_audio() once a custom model is employed for speech input.

            # Example Usage
            analyzer = EmotionAnalyzer()
            sentiment = analyzer.analyze_text("The product has exceeded my expectations!")
            print("Sentiment Analysis Result:", sentiment)
            

System Integration

• AI Assistants: Enhances conversational agents with emotional intelligence to respond empathetically.
• Personalized Systems: Integrates with ai_personality_module.py for adaptive user modeling.
• Monitoring and Reporting: Works with ai_advanced_reporting.py to generate emotional trends over time.

Future Enhancements

• Advanced Multimodal Emotion Recognition: Add ensemble models for seamless cross-modal emotion analysis.
• Domain-Specific Emotional Tuning: Customize sentiment/emotion models for specific industries (e.g., healthcare, entertainment).
• Real-Time Integration: Enable low-latency, real-time emotion streaming for interactive AI applications.