nlu-sentiment.md

copyright	lastupdated	keywords	subcollection
years 2019, 2024	2023-03-13	Watson NLP, entities, keywords, pos, part of speech, sentiment	discovery-data

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Detect sentiment

{: #nlu-sentiment}

Use the built-in Watson Natural Language Processing (NLP) sentiment enrichment to analyze the sentiment that is expressed in text and indicate whether the text is positive, neutral, or negative. {: shortdesc}

To understand the sentiment of an entire document, apply this enrichment to a field that contains as much of the text from the document as possible, such as the text field.

To analyze sentiment in text from multiple fields at one time and capture the overall sentiment of the document, use the Content Mining application. For more information, see Detecting phrases that express sentiment.

Adding the enrichment

{: #nlu-sentiment-task}

To add the sentiment enrichment, complete the following steps:

1. Open your project and go to the Manage collections page.

2. Click to open the collection that you want to enrich.

3. Open the Enrichments tab.

4. Scroll to find and select the Sentiment enrichment.

5. Choose one or more fields to apply the enrichment to.

   You can apply enrichments to the text and html fields, and to custom fields that were added from uploaded JSON or CSV files or from the Smart Document Understanding (SDU) tool.

6. Click Apply changes and reprocess.

Enrichments that you enable are applied to the documents in random order. For information about how to remove an enrichment, see Managing enrichments.

Example

{: #nlu-sentiment-example}

Input

{: #nlu-sentiment-example-input}

"It is powerful and easy to use and integrate with third party applications."

{: codeblock}

Response

{: #nlu-sentiment-example-response}

In the JSON output:

• score = Sentiment score from -1 (negative) to 1 (positive)
• label = positive, negative, or neutral
• mixed = Indicates that the document expresses a combination of different sentiments

{
  "sentiment": {
    "score": 0.9255063900060722,
    "mixed": false,
    "label": "positive"
  }
}

{: codeblock}