# Enrich with Hyperscaler API
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/enrich-hyper.html

> Call the GCP API and enrich a modeling dataset that predicts customer churn.

# Enrich with Hyperscaler API

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/data_enrichment_gcp_nlp_api/GCP_enrich_sentiment.ipynb)

Many companies are recognizing the value of unstructured data, particularly in the form of text, and are looking for ways to extract insights from it. This data includes emails, social media posts, customer feedback, call transcripts, and more. One of the most powerful tools for analyzing text data is sentiment analysis.

Sentiment analysis is the process of identifying the emotional tone of a piece of text, such as positive, negative, or neutral. It is a valuable tool to enrich the dataset for building machine learning models. For example, the sentiment expressed through a customer's recent call transcript with customer service could be predictive of the customer's likelihood to churn.

However, building sentiment analysis models is not an easy task. It requires a significant investment of time, resources, and expertise, especially in terms of accurately labeled data with large corpus to train. Most companies do not have the resources or expertise to develop their own sentiment analysis models.

Fortunately, there are now APIs available that provide sentiment analysis as a service. By using these APIs, companies can save time and money while still gaining the benefits of sentiment analysis. One of the most significant benefits of using hyperscaler APIs for sentiment analysis is their accuracy. The models behind the APIs are trained on large amounts of data, making them highly accurate at identifying emotions and sentiments in text data.

This accelerator demonstrates how easy it is to call the GCP API and enrich a customer churn prediction modeling dataset. An improvement in the model performance is based on retrieved sentiment scores from customer call transcripts.

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