Twitter Sentiment Analysis using LLM

Prompt Strategies: Reliability & Reasoning

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📌 Project Overview

This project performs sentiment analysis using a Large Language Model (Gemini) with advanced prompt engineering strategies.

Two analytical dimensions are implemented:

• 🔎 Reasoning → Detailed explanation of why a sentiment was chosen
• 📊 Reliability Score → Confidence estimation (0.0 – 1.0)

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🧠 Approach

Instead of training deep learning models, this project leverages:

• Structured Prompt Engineering
• Deterministic temperature (0)
• JSON-constrained output
• Reliability scoring

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📝 Prompt Strategy

The model is instructed to:

• Return strictly valid JSON
• Keep order of input texts
• Provide analytical reasoning
• Assign reliability score

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📂 Project Structure

twitter-sentiment-llm/ │ ├── data/ │ ├── input.txt │ └── output.json │ ├── main.py ├── .env.example ├── requirements.txt └── README.md

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🚀 How to Run

1. Clone the repo

2. Create .env file based on .env.example

3. Install dependencies pip install -r requirements.txt

4. Run: python main.py

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🛠 Technologies Used

• LangChain
• Google Gemini
• Prompt Engineering
• Python

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🎯 Key Difference from Traditional ML

Traditional ML	LLM Prompt Strategy
Requires training	No training
Fixed architecture	Flexible reasoning
Accuracy-based	Explanation-based

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👤 Author

Mohamed Mahmoud