This project focuses on detecting psychological manipulation patterns in conversations using Natural Language Processing and Machine Learning techniques.
The solution includes text preprocessing, vectorization, exploratory analysis, model training, and classification performance evaluation for manipulation-related language patterns.
- Natural Language Processing workflow implementation
- Text preprocessing and cleaning
- TF-IDF text vectorization
- Manipulative language classification
- Exploratory text analysis
- Model training and evaluation
- Class distribution analysis
- Performance metric comparison
The project includes the following stages:
The preprocessing pipeline includes:
- Text cleaning
- Lowercasing
- Missing value handling
- Removal of unnecessary characters
- Text normalization
Text data was transformed using:
- TF-IDF vectorization
- Numerical text representation
- Sparse feature matrices for machine learning models
The project focuses on training classification models capable of identifying manipulation patterns in conversations.
The workflow includes:
- Model training
- Prediction generation
- Classification evaluation
- Model comparison
The project uses the following Kaggle dataset:
Psychological Manipulation Conversations Dataset
https://www.kaggle.com/datasets/tatheerabbas/psychological-manipulation-conversations-dataset
The dataset is not included in this repository because of its size.
The project analyzes:
- Manipulative language patterns
- Class distribution
- Text frequency characteristics
- Linguistic patterns in conversations
- Classification effectiveness for manipulation detection
- Python
- Pandas
- NumPy
- Scikit-learn
- Natural Language Processing
- TF-IDF Vectorization
- Matplotlib
- Seaborn
- Jupyter Notebook
The goal of this project is to demonstrate practical Natural Language Processing and Machine Learning skills for detecting manipulative communication patterns in textual data.
The solution successfully demonstrates:
- End-to-end NLP workflow implementation
- Text preprocessing and feature engineering
- TF-IDF vectorization
- Classification model training
- Manipulation pattern analysis
- Machine learning evaluation workflows
- Data exploration and visualization
- Practical NLP pipeline development
Paulina Broda