Matt Eland tries out the TextAnalytics client:
We’ll talk about each one of these capabilities briefly as we cover the results, but at a high level what we want to do is:
- Perform sentiment analysis to determine if the text is positive, negative, neutral, or mixed.
- Summarize the text using abstractive summarization which summarizes the text with new text generated by a large language model (LLM).
- Summarize the text using extractive summarization which summarizes the text by extracting key sentences or parts of sentences to convey the overall meaning.
- Extract key phrases of interest from the text document.
- Perform entity recognition and linked entity recognition to determine the major objects, places, people, and concepts the document discusses.
- Recognize any personally identifiable information (PII) present in the document for potential redaction.
- Analyze the text for healthcare specific topics such as treatment plans or medications.
Read on to see how a certain passage of text fares.