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In this example, the code first preprocesses the text, which is an example of where the hypothetical **PSE** can be used. Then, it uses **Watson NLP** to analyze the sentiment. The sentiment analysis output (positive, negative, or neutral) is then printed to the console. Next, we can move on to a task such as **entity recognition**. Entity recognition involves identifying and classifying named entities in text, such as people, organizations, locations, and dates. Let’s modify our `analyze_sentiment` function to also include entity recognition:
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* **Supply Chain**: Businesses use unique identifiers to track products throughout the supply chain, from manufacturing to delivery.
