This project aims to detect emotional manipulation in social media advertisements using data analytics and machine learning techniques. With the rapid growth of digital marketing, advertisers often use emotional cues to influence users’ decisions. This project analyzes text-based ad content to classify and identify emotionally manipulative patterns, promoting ethical and transparent advertising practices.
- Identify and quantify emotional manipulation in advertisements.
- Apply data analytics, NLP, and machine learning to detect persuasive emotional tones.
- Visualize emotional trends and manipulation intensity across datasets.
- Encourage ethical communication practices in digital marketing.
- Data Preprocessing: Cleans and structures social media ad datasets.
- Text Analytics: Tokenization, lemmatization, and sentiment analysis.
- Feature Engineering: Extraction of linguistic and emotional cues.
- Modeling: Classification using ML algorithms (e.g., Logistic Regression, Random Forest, SVM).
- Visualization: Emotion distribution plots and model performance metrics.
- Ethical Insights: Interpretation of manipulative versus neutral ad content.
| Category | Tools / Libraries |
|---|---|
| Programming Language | Python |
| Data Processing | Pandas, NumPy |
| NLP | NLTK, SpaCy, TextBlob |
| Machine Learning | Scikit-learn |
| Visualization | Matplotlib, Seaborn |
| Environment | Jupyter Notebook |
The dataset consists of social media advertisements labeled according to their emotional tone (e.g., fear, happiness, guilt, excitement). Each entry contains:
Ad_Text— textual content of the adEmotion_Label— detected or assigned emotional categoryManipulation_Score— level of manipulation (0–1 scale)
🔹 Dataset can be customized or replaced with publicly available ad datasets (e.g., Kaggle or scraped social media data).
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Data Collection & Preprocessing
- Text cleaning, stopword removal, tokenization.
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Exploratory Data Analysis (EDA)
- Frequency analysis, sentiment trends, emotion distribution.
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Feature Engineering
- TF-IDF, sentiment polarity, subjectivity features.
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Model Building & Evaluation
- Train/test split, accuracy, F1-score, confusion matrix.
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Result Visualization
- Emotional trends, model performance charts.
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Interpretation & Ethical Analysis
- Identifying manipulative ad traits and insights.
- Accuracy achieved: ~85% (varies by model and dataset)
- Emotional categories identified: Fear, Guilt, Happiness, Sadness, Anger
- Key indicators: Use of emotional keywords, urgency cues, and sentiment polarity.
This project emphasizes AI ethics in marketing analytics by:
- Promoting transparent ad content.
- Identifying psychological exploitation in advertisements.
- Supporting user awareness and informed decisions.
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Clone this repository:
git clone https://github.com/yourusername/Emotional-Manipulation-Detector.git cd Emotional-Manipulation-Detector -
Install dependencies:
pip install -r requirements.txt
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Open the Jupyter notebook:
jupyter notebook "Emotional_Manipulation_Detector.ipynb" -
Run all cells sequentially to see results.
- Integration with real-time ad monitoring APIs.
- Deep learning models (BERT, RoBERTa) for emotion detection.
- Development of a dashboard or web app for ad scanning.
This project is licensed under the MIT License.