Conscious Dating App Module - Behavioral Analysis & NLP

📌 Project Overview

This project aims to analyze user behaviors and preferences in a dating app using Natural Language Processing (NLP), Machine Learning, and Behavioral Analytics. The ultimate goal is to provide data-driven insights that enhance user experience and match-making accuracy.

🎯 Objectives

• Understand user behaviors through NLP and sentiment analysis.
• Perform clustering to identify user personas.
• Build a chatbot that provides insights on user profiles based on their attributes.

📊 Dataset Information

• Source: Datasets consist of user-generated responses and profile information from a dating platform and an independent survey.
• Size: ~60,000 user profiles.
• Features:
  • Demographics: Gender, age, height, education, income, smoking/drinking habits, etc.
  • Behavioral Insights: Ghosting experience, dating preferences, punctuality, etc.
  • NLP Features: User-written essays about self-summary, favorite activities, values, etc.

🔬 Methodology

1️⃣ Data Preprocessing

• Combined all text-based fields into a unified "user_profile" column.
• Cleaned and lemmatized text for NLP tasks.
• Handled missing values and categorical encodings.

2️⃣ NLP & Embeddings

• Used BERT transformer model to generate embeddings.
• Applied KMeans clustering to group users based on their text similarity.

3️⃣ Behavioral Analysis & Classification

• Extracted behavioral tendencies (respect, ethics, communication, growth) from text using keyword-based and deep learning methods.

4️⃣ Chatbot Implementation

• The chatbot analyzes a user's profile and provides insights & predictions.
• Implemented using OpenAI GPT API for personalized recommendations.

🛠 Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)

• NLP Models: BERT, Sentence-Transformers

• Machine Learning: Scikit-Learn (KMeans)

• Chatbot: OpenAI GPT API

📌 Future Improvements

• Implement real-time behavioral analysis in a dating app.
• Improve chatbot explanations by integrating external behavioral psychology research.
• Optimize NLP embeddings to reduce processing time.
• Apply Deep Learning-based sentiment analysis to refine emotional tendencies  further.
• Understand how to apply behavioral tendencies to the model.

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For more details, contact Selinsight or open an issue in the repository!