🌟 ViveVocal: Azure Customer Review Analysis 🌟

🚀 Project Overview

ViveVocal is an AI-driven platform that transforms customer feedback into valuable insights using Azure Speech Services for speech-to-text conversion and Azure Language Services for sentiment analysis, language detection, key phrase extraction, and entity recognition. This project aims to help businesses better understand and act on customer feedback by providing real-time insights from reviews submitted through voice, file uploads, or text.

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🌟 Key Features

• 🗣️ Speech Input: Converts spoken feedback into text using Azure Speech Services.

• 📁 File Upload: Analyzes audio or text file uploads.

• ⌨️ Text Input: Direct text review analysis.

• 🎭 Sentiment Analysis: Detects the emotional tone (positive, negative, neutral).

• 🌍 Language Detection: Automatically detects the language of the review.

• 🔑 Key Phrase Extraction: Extracts important topics and key phrases.

• 🏷️ Entity Recognition: Identifies and links key entities like products, services, locations, and competitors to relevant sources.

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🔑 Why It's a Game Changer

By analyzing key phrases and entities, businesses can:

• 🎯 Address specific customer concerns.
• 📊 Spot emerging trends.
• 🚀 Respond proactively based on real-time feedback.

Understanding not only the emotional tone but also how entities (products, brands, services) impact customer opinions provides businesses with a unique edge in improving customer satisfaction and making data-driven decisions.

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⚙️ How It Works

1. Speech-to-Text: Converts audio feedback into text using Azure Speech Services.
2. Text Processing: Processes the text using Azure Language Services for:
   • Sentiment analysis.
   • Language detection.
   • Key phrase extraction.
   • Entity detection.
3. Output: Provides the sentiment, key phrases, detected language, and links to relevant entities.

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🛠️ Installation

'''

1. Clone the Repository

git clone https://github.com/priyans877/VIvaVocal-Azure-Customer-Review-Analysis-.git
cd VIvaVocal-Azure-Customer-Review-Analysis-

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2. Install Dependencies

--bash pip install -r requirements.txt

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3. Set Up Azure Services

Create an Azure Speech Services resource for speech-to-text conversion. Set up Azure Language Services for sentiment analysis, language detection, key phrase extraction, and entity recognition. Update your configuration files with the Azure API keys. 🚀 Usage

Run the Application:

streamlit run app.py

Provide Reviews in One of Three Ways:

• 🗣️ Speak into the microphone.
• 📁 Upload a file (audio or text).
• ⌨️ Type your review.

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Analyze the Output:

• View the sentiment (positive, neutral, negative).
• Key phrases and entities are displayed for further insights.
• Get language detection results and entity links for additional context.

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💻 Technology Stack

• Azure Speech Services: Converts speech to text.
• Azure Language Services: Provides sentiment analysis, key phrase extraction, and entity detection.
• Python: Main programming language.
• Streamlit: Front-end for the web interface.

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🤝 Contributing

We welcome contributions! To get started:

Fork the repository.

• Create a new branch for your feature.
• Submit a pull request.
• Feel free to open issues for suggestions or bug reports.

📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

🎓 Acknowledgements

Special thanks to ICT Academy and Infosys for providing the training that made this project possible.

🌐 Connect

If you have any questions or feedback, feel free to contact us or open an issue in the GitHub repository!