{"id":1227,"date":"2018-09-04T19:05:06","date_gmt":"2018-09-04T23:05:06","guid":{"rendered":"https:\/\/blog2wrkdev.wpenginepowered.com\/automated-sentiment-analysis\/"},"modified":"2026-09-29T06:44:39","modified_gmt":"2026-09-29T10:44:39","slug":"automated-sentiment-analysis","status":"publish","type":"post","link":"https:\/\/blog2workato.wpengine.com\/automated-sentiment-analysis\/","title":{"rendered":"Everything You Wanted to Know about Automated Sentiment Analysis for Better Customer Ops"},"content":{"rendered":"\nDo you often wish you knew exactly what your customers are thinking?\n\nIs your support team constantly burned out from dealing with the ups and downs of client interaction?\n\nYou\u2019re not alone. Businesses around the world know that their success relies on being able to interpret their customers’ thoughts and\u2014more importantly\u2014their feelings. But given the complexity of human communication, that can be a tall order. Customer-facing employees are frequently stuck reacting to customers in the moment rather than offering up tailored, proactive engagement.\n\nEnter sentiment analysis. A relatively new application of technology, it promises to change the face of customer interaction forever. But how does it work? Can it really live up to its promise of making you more in tune with your customers?\n\nRelated: How automation can transform the way your organization supports clients<\/a><\/em>\n

How does sentiment analysis work?<\/h3>\nEven though it sounds complex and futuristic, sentiment analysis isn\u2019t difficult to understand. On a fundamental level, it\u2019s a combination of two other emerging technologies: Natural Language Processing (NLP) and artificial intelligence (AI).\n\nNLP is a subset of AI that lets machines understand human speech, the way we actually talk. So instead of only understanding a predetermined set of rigidly expressed commands, machines can comprehend a wider range of inputs that are much less structured. It\u2019s the difference between an automated phone system telling you to \u201cPress 1 for technical support\u201d versus \u201cTell me what you\u2019re calling about today.\u201d\n\nAI tools like the Watson Tone Analyzer<\/a> use NLP to parse text\u2014such as tweets, support tickets, or emails\u2014and score it on a variety of emotions. In the case of Watson, these scores happen at both a document level (what is the overall tone of the text?) and at a sentence level\u00a0so you can see exactly where those sentiments are expressed.\n\nBecause text often contains more than one emotion or tone, these scores are expressed as percentages. You can see the full range of emotions present in the text ranked according to which are most dominant. And since AI is constantly learning, the results get more accurate over time as the tool analyzes a wider body of text.\n

Why is sentiment analysis useful to my business?<\/h3>\nThere are lots of creative ways to use sentiment analysis. Some of the most common use cases include:\n