Project Background:
Visual Sentiment Analysis is rooted in the increasing volume of visual content shared on digital platforms, from images on social media to videos in online advertising. Understanding the emotions and sentiments conveyed by these visuals is essential for businesses, investigators, and content creators. This project aims to automatically recognize and analyze the emotional tone and sentiments expressed in images and videos, which range from joy and excitement to sadness and anger. This technology has a wider broad spectrum of applications, from improving advertising and content personalization to assisting in sentiment-aware product design and assessing the impact of visual content on user engagement.
Problem Statement
- The visual sentiment analysis addresses the challenge of automatically recognizing and understanding the emotional content conveyed in images and videos.
- With the exponential growth of visual content on digital platforms, there is a pressing need to develop robust and accurate sentiment analysis models for visuals.
- The challenging problem extends to applications like advertising and mental health assessment, where understanding the emotional impact of visual content is crucial.
- This is pivotal for enhancing the emotional resonance of digital communication, enabling businesses and content creators to engage with users more profoundly through visually resonant and emotionally attuned content.
- However, it involves overcoming several hurdles, including the subjectivity of sentiment interpretation needed for large-scale and annotated datasets.
Aim and Objectives
- To develop automated systems for accurately recognizing and understanding the emotional sentiments conveyed in visual content, enhancing emotional resonance in digital communication.
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To create deep learning models capable of recognizing a broad spectrum of emotions in images and videos.
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To develop methods considering contextual cues and cultural variations in sentiment interpretation.
- To curate and utilize large-scale, annotated datasets for training and evaluation.
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To apply Visual Sentiment Analysis in content recommendation, advertising, and mental health assessment.
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To enhance user engagement by creating emotionally resonant visual content.
Contributions to Visual Sentiment Analysis
1. In this project, the visual sentiment analysis enables personalized content recommendations, improving user engagement and satisfaction.
2. It provides data-driven insights into the emotional impact of visual content, allowing for informed decision-making in various domains.
3. It fosters emotionally resonant user experiences by tailoring content to the sentiments and preferences of individuals.
4. It also contributes to mental health applications by assessing and monitoring emotional states from visual cues, aiding early intervention and support.
Deep Learning Algorithms for Visual Sentiment Analysis
- Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory (LSTM) networks
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Generative Adversarial Networks (GANs)
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Convolutional-LSTM networks
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Transformers
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Multimodal Learning Models
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Attention Mechanisms
Datasets for Visual Sentiment Analysis
- EmoReact
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SEMAINE
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SentiBank
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EmoReact
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Plutchiks Wheel of Emotions
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AffectNet
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EmoReact
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ADE20K
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EmoReact
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MELD (Multimodal EmotionLines Dataset)
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Friends TV Show Dataset
Performance Metrics
- Accuracy
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Mean Absolute Error (MAE)
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Mean Squared Error (MSE)
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Root Mean Squared Error (RMSE)
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F1 Score
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Area Under the Receiver Operating Characteristic (ROC-AUC)
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Area Under the Precision-Recall Curve (PR-AUC)
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Mean Opinion Score (MOS)
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Kappa statistic
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Concordance Correlation Coefficient (CCC)
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
