ComplaintAI

AI-Powered Customer Complaint Prioritization

ComplaintAI – AI-Powered Customer Complaint Prioritization 🌟 Imagine This... You're the head of customer experience at a telecom company, and your support center is flooded with hundreds of customer complaints every day.

Some customers report minor issues—a slight network slowdown or a billing question. Others are experiencing critical problems—dropped calls, poor data speeds, or service outages that could lead to customer churn. Your network engineers also provide KPI data, showing where the network is struggling—but this information isn't linked to complaints. How do you decide which complaints to address first? 🔥

What if you could intelligently rank complaints based on: ✅ Customer sentiment – Are they angry, neutral, or happy? ✅ Technical impact – Do the complaints align with poor network KPIs? ✅ Severity prediction – Can AI classify complaints into high, medium, or low priority? 🚀 Introducing ComplaintAI 🚀 ComplaintAI is an AI-driven complaint management system that helps telecom companies prioritize customer complaints intelligently.

✅ Uses NLP (BERT) & Machine Learning (XGBoost) to analyze complaints. ✅ Predicts severity based on sentiment + network KPIs. ✅ Automatically ranks complaints so urgent issues are handled first. ✅ Deployable as an API to integrate with support ticketing systems.

🎯 How It Works 1️⃣ Collect Data 🗂️ 📥 Loads customer complaints & network KPIs from CSV or database.

2️⃣ Analyze Sentiment 🤖 🧠 Uses BERT NLP model to determine customer sentiment (Positive/Negative).

3️⃣ Engineer Features 🏗️ 🔍 Extracts insights using TF-IDF (text embeddings), complaint length, and network KPIs.

4️⃣ Train AI Model 🏆 📊 Uses XGBoost to predict complaint severity (High, Medium, Low).

5️⃣ Prioritize Issues 🔥 📌 Ranks complaints based on severity, sentiment, and technical impact.

6️⃣ Deploy as an API 🌍 🔗 The system can be integrated into customer service platforms (via FastAPI).

🛠️ Installation 1️⃣ Clone the repo:

bash Copy Edit git clone https://github.com/yourusername/ComplaintAI.git cd ComplaintAI 2️⃣ Install dependencies:

bash Copy Edit pip install -r requirements.txt 3️⃣ Run the main workflow:

bash Copy Edit python main.py 4️⃣ Start the API (for integration with other services):

bash Copy Edit uvicorn api:app --reload 🧪 Example Usage Sample Input (Customer Complaints) complaint_id area_id complaint_text 101 A1 "The network keeps dropping, so frustrating!" 102 A2 "The speed is a bit slow, but manageable." 103 A3 "My calls keep dropping, I might switch providers." Sample Input (Network KPI Data) area_id access_issue drop_call_rate voice_quality_score data_throughput A1 1 5.2% 2.1 1.5 Mbps A2 0 1.1% 4.5 15 Mbps A3 1 6.7% 1.8 0.8 Mbps Predicted Output (Prioritized Complaints) complaint_id complaint_text sentiment severity priority_score 103 "My calls keep dropping..." Negative High 🚨 Urgent 101 "Network keeps dropping..." Negative Medium ⏳ Needs Attention 102 "Speed is a bit slow..." Positive Low ✅ Can Wait 💡 Now your customer service team knows exactly which issues to resolve first!

🖥️ API Usage (For Integration) Start the API with:

bash Copy Edit uvicorn api:app --reload Then send a POST request with complaint data:

json Copy Edit { "complaint_id": 104, "area_id": "A1", "complaint_text": "My internet is not working, terrible service!" } The API returns a predicted severity score and priority ranking.

📊 Why This Matters 🚀 Faster issue resolution → Customers get help before churning. 🎯 Data-driven prioritization → AI helps support teams focus on urgent cases. ⚡ Smarter decision-making → Network teams can fix technical issues based on complaints. 📡 Scalable & deployable → Can integrate with customer service chatbots or CRM tools. 🔧 Future Enhancements ✅ Deploy as a cloud-based microservice. ✅ Use LLMs (GPT, T5) for even better complaint understanding. ✅ Train a deep learning model (LSTM or Transformer-based). ✅ Extend to voice-based complaints (Speech-to-Text + NLP Analysis). 📜 Project Files & Modules File Description data_loader.py Loads customer complaints & KPI data sentiment_analysis.py Uses BERT for Sentiment Analysis feature_engineering.py Extracts features (TF-IDF, KPIs, complaint length) classifier.py Trains XGBoost model to predict severity prioritization.py Predicts and ranks complaints based on urgency api.py Provides API for real-time severity predictions main.py Runs the full pipeline 👨‍💻 Contributing Feel free to contribute!

Fork the repo Create a feature branch (git checkout -b new-feature) Commit changes (git commit -m "Added feature") Push branch (git push origin new-feature) Open a Pull Request 🎉 📩 Contact & Credits Built by Hossein Sabbaghpour 💡 🚀 Connect on LinkedIn 🌍 See more projects on GitHub

🌟 Final Thoughts If you’ve ever lost a customer due to slow complaint resolution, this project can help telecom companies become proactive instead of reactive. 🚀

Let AI handle the prioritization, so your customers stay happy! 😊