🔍 AI-Powered MBTI: Analyzing Personality with Machine Learning
🚀 Exploring Personality Through AI
In recent years, AI and psychology have started converging in fascinating ways. One area I’ve been exploring is using machine learning to analyze and predict MBTI personality types based on data-driven insights.
As someone passionate about algorithms, data, and AI, I wanted to see how well AI could classify MBTI types using text analysis, statistical models, and deep learning. This post dives into the methodology, challenges, and insights from my work.
🔢 How Does AI Predict Personality?
1️⃣ Data Collection & Preprocessing
To train an AI to classify MBTI types, we need data from text samples, preferably from social media, blogs, or structured MBTI datasets.
Scraped public MBTI-labeled datasets (e.g., Reddit, Twitter, Kaggle datasets).
Preprocessed text (tokenization, stopword removal, lemmatization).
Vectorized data using TF-IDF and word embeddings (Word2Vec, BERT).
2️⃣ Feature Engineering
To improve prediction accuracy, I experimented with various NLP features:
✅ Sentence structure, lexical richness, and tone analysis
✅ Use of introvert vs. extrovert language patterns
✅ Semantic similarity clustering with Word2Vec & transformer models
3️⃣ Model Selection & Training
I tested multiple machine learning and deep learning models:
📊 Naïve Bayes & Logistic Regression – Quick baseline models.
🤖 Random Forest & SVM – Performed well for structured MBTI features.
🧠 BERT-based transformers – Provided deeper context understanding.
✅ The best-performing model used BERT fine-tuning, achieving higher accuracy in distinguishing personality types from raw text.
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