Analyze Text With Azure AI Language (Part 2)

By Ashwin Venugopal -

 







Detect Language

After analyzing text input, the Azure AI Language Detection API returns language IDs together with a score that indicates how strong the analysis was. Content storage that gather arbitrary text in situations where the language is uncertain can benefit from this feature. A chatbot could be involved in another situation. Language recognition can be used to identify the language a user is speaking when they initiate a chat session with the chatbot, enabling you to set up your responses in the relevant language.

The language employed in the input document can be ascertained by parsing the analysis's findings. Additionally, the response provides a score (a number between 0 and 1) that represents the model's level of confidence, with values closer to 1 being a higher confidence level. Documents or individual phrases might be used for language detection. It's crucial to remember that the paper must not exceed 5,120 characters. Each collection is limited to 1,000 items (IDs), and the size restriction is per document.

The service will act somewhat differently if you submit a document with multilingual content.
When a document contains mixed language content, the language that is most prevalent in the content is given a lower positive rating, which indicates the assessment's marginal strength.

The last condition to consider is when there is ambiguity as to the language content. The scenario might happen if you submit textual content that the analyzer is not able to parse. As a result, the response for the language name and ISO code will indicate (unknown) and the score value will be returned as 0.

Extract Key Phrases

The technique of analyzing a document's or documents' text and then determining the key points related to the document's context is known as key phrase extraction.
Larger papers are ideal for key word extraction (the maximum size that may be examined is 5,120 characters). You can submit one or more documents for analysis via the REST service, just like with language detection.

Analyze Sentiment

Sentiment analysis is used to evaluate how positive or negative a text document is, which can be useful in various workloads, such as:

  • Evaluating a movie, book, or product by quantifying sentiment based on reviews.

  • Prioritizing customer service responses to correspondence received through email or social media messaging.

When using Azure AI Language to evaluate sentiment, the response includes overall document sentiment and individual sentence sentiment for each document submitted to the service. Sentence sentiment is based on confidence scores for positive, negative, and neutral classification values between 0 and 1. Overall document sentiment is based on sentences:

  • If all sentences are neutral, the overall sentiment is neutral.

  • If sentence classifications include only positive and neutral, the overall sentiment is positive.

  • If the sentence classifications include only negative and neutral, the overall sentiment is negative.

  • If the sentence classifications include positive and negative, the overall sentiment is mixed.

Conclusion

We have successfully learnt about detecting language, analyze sentiment, and extracting key phrases.

 

 









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