Sentiment - Amazon Comprehend


Use Amazon Comprehend to determine the sentiment of content in UTF-8 encoded text documents. For example, you can use sentiment analysis to determine the sentiments of comments on a blog posting to determine if your readers liked the post.

You can determine sentiment for documents in any of the primary languages supported by Amazon Comprehend. All documents in one job must be in the same language.

Sentiment determination returns the following values:

  • Positive – The text expresses an overall positive sentiment.

  • Negative – The text expresses an overall negative sentiment.

  • Mixed – The text expresses both positive and negative sentiments.

  • Neutral – The text does not express either positive or negative sentiments.

You can use any of the following API operations to detect the sentiment of a document or a set of documents.

The operations return the most likely sentiment for the text and the scores for each of the sentiments. The score represents the likelihood that the sentiment was correctly detected. For example, in the example below it is 95 percent likely that the text has a Positive sentiment. There is a less than 1 percent likelihood that the text has a Negative sentiment. You can use the SentimentScore to determine if the accuracy of the detection meets the needs of your application.

The DetectSentiment operation returns an object that contains the detected sentiment and a SentimentScore object. The BatchDetectSentiment operation returns a list of sentiments and SentimentScore objects, one for each document in the batch. The StartSentimentDetectionJob operation starts an asynchronous job that produces a file containing a list of sentiments and SentimentScore objects, one for each document in the job.

The following example is the response from the DetectSentiment operation.

{ "SentimentScore": { "Mixed": 0.030585512690246105, "Positive": 0.94992071056365967, "Neutral": 0.0141543131828308, "Negative": 0.00893945890665054 }, "Sentiment": "POSITIVE", "LanguageCode": "en" }