Sentiment Analysis using transformer models is an advanced natural language processing (NLP) solution hosted on Azure that allows businesses to analyze and interpret the emotional tone of text data. This product leverages state-of-the-art machine learning techniques to provide accurate sentiment classification, helping organizations make informed decisions based on customer feedback and social media interactions.

• Process and analyze text data in real-time to capture immediate sentiment trends.
• Analyze text in various languages to cater to a global audience.
• Fine-tune sentiment analysis models to fit specific industry needs and terminologies.
• Seamlessly integrate with Azure Machine Learning, Azure Logic Apps, and other Azure services for enhanced workflows.

To set up and test the sentiment analysis model, follow these steps:

python3 -m venv venv
source venv/bin/activate

To test the example, create a Python file:

nano sentiment_transformer.py

Then, add the following code to sentiment_transformer.py:

from transformers import pipeline

classifier = pipeline("sentiment-analysis")
text = "I hate waiting in lines."
result = classifier(text)
print(f"Text: {text}")
print(f"Sentiment: {result[0]['label']}, Score: {result[0]['score']:.2f}")

Finally, run the script:

python3 sentiment_transformer.py