{"id":1460,"date":"2019-02-17T09:00:42","date_gmt":"2019-02-17T03:30:42","guid":{"rendered":"https:\/\/www.rangakrish.com\/?p=1460"},"modified":"2019-02-17T16:11:24","modified_gmt":"2019-02-17T10:41:24","slug":"identifying-emotions-from-text","status":"publish","type":"post","link":"https:\/\/www.rangakrish.com\/index.php\/2019\/02\/17\/identifying-emotions-from-text\/","title":{"rendered":"Identifying Emotions from Text"},"content":{"rendered":"

Identifying the predominant sentiment<\/strong><\/em> in unstructured text is used widely these days. There are several REST API services that allow you to submit a piece of text and get back the corresponding sentiment analysis. Meaningcloud<\/strong><\/em><\/a>, Aylien<\/strong><\/em><\/a>, Google’s Cloud Natural Language API<\/strong><\/em><\/a>, and\u00a0 <\/span>IBM Natural Language Understanding<\/strong><\/em>\u00a0Service<\/strong><\/em><\/a> are just a few.<\/p>\n

Emotion<\/strong><\/em> detection, especially from facial expressions and speech, is also catching up. Google<\/strong><\/em><\/a> and Microsoft<\/strong><\/em><\/a> have APIs to do this. However, for my article today, the focus is on detecting emotions from “unstructured text<\/strong><\/em>“, and I am going to look at IBM’s API as part of its Natural Language Understanding Service.<\/strong><\/em><\/a><\/p>\n

By the way, if you are interested in learning about the differences between “sentiment<\/strong><\/em>” and “emotion<\/strong><\/em>“, I encourage you to go through this<\/strong><\/em><\/a>\u00a0article and this<\/strong><\/em><\/a>\u00a0one.<\/p>\n

The set of emotions detected by different APIs varies somewhat. IBM<\/strong><\/em>, for instance, detects:<\/p>\n

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