{"id":1944,"date":"2022-03-09T22:40:13","date_gmt":"2022-03-09T22:40:13","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/03\/09\/extract-granular-sentiment-in-text-with-amazon-comprehend-targeted-sentiment\/"},"modified":"2022-03-09T22:40:13","modified_gmt":"2022-03-09T22:40:13","slug":"extract-granular-sentiment-in-text-with-amazon-comprehend-targeted-sentiment","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/03\/09\/extract-granular-sentiment-in-text-with-amazon-comprehend-targeted-sentiment\/","title":{"rendered":"Extract granular sentiment in text with Amazon Comprehend Targeted Sentiment"},"content":{"rendered":"
\n

Amazon Comprehend<\/a> is a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text. As a fully managed service, Amazon Comprehend requires no ML expertise and can scale to large volumes of data. Amazon Comprehend provides several different APIs<\/a> to easily integrate NLP into your applications. You can simply call the APIs in your application and provide the location of the source document or text. The APIs output entities, key phrases, sentiment, document classification, and language in an easy-to-use format for your application or business.<\/p>\n

The sentiment analysis APIs provided by Amazon Comprehend help businesses determine the sentiment of a document. You can gauge the overall sentiment of a document as positive, negative, neutral, or mixed. However, to get the granularity of understanding the sentiment associated with specific products or brands, businesses have had to employ workarounds like chunking the text into logical blocks and inferring the sentiment expressed towards a specific product.<\/p>\n

To help simplify this process, starting today, Amazon Comprehend is launching the Targeted Sentiment<\/a> feature for sentiment analysis. This provides the capability to identify groups of mentions (co-reference groups) corresponding to a single real-world entity or attribute, provide the sentiment associated with each entity mention, and provide the classification of the real-world entity based on a pre-determined list of entities<\/a>.<\/p>\n

This post provides an overview of how you can get started with Amazon Comprehend targeted sentiment, demonstrates what you can do with the output, and walks through three common targeted sentiment use cases.<\/p>\n

Solution overview<\/h2>\n

The following is an example of targeted sentiment:
\"\"<\/a><\/p>\n

\u201cSpa\u201d is the primary entity, identified as type facility<\/code>, and is mentioned two more times, referred to as the pronoun \u201cit.\u201d The Targeted Sentiment API provides the sentiment towards each entity. Positive sentiment is green, negative is red, \u00a0and neutral is blue. We can also determine how the sentiment towards the spa changes throughout the sentence. We dive deeper into the API later in the post.<\/p>\n

This capability opens up several different capabilities for businesses. Marketing teams can track popular sentiments toward their brands in social media over time. Ecommerce merchants can understand which specific attributes of their products were best- and worst-received by customers. Call center operators can use the feature to mine transcripts for escalation issues and to monitor customer experience. Restaurants, hotels, and other hospitality industry organizations can use the service to turn broad ratings categories into rich descriptions of good and bad customer experiences.<\/p>\n

Targeted sentiment use cases<\/h2>\n

The Targeted Sentiment API in Amazon Comprehend takes text data such as social media posts, application reviews, and call center transcriptions as input. Then it analyzes the input using the power of NLP algorithms to extract entity-level sentiment automatically. An entity<\/em> is a textual reference to the unique name of a real-world object, such as people, places, and commercial items, in addition to precise references to measures such as dates and quantities. For a full list of supported entities, refer to Targeted Sentiment Entities<\/a>.<\/p>\n

We use the Targeted Sentiment API to enable the following use cases:<\/p>\n