![]() If this text data is gathered, collated, structured, and analyzed correctly, valuable knowledge can be derived from it. Most of the text data is unstructured and scattered around the web. Besides, most customer interactions are now digital, which creates another huge text database. An enormous amount of text data is generated every day in the form of blogs, tweets, reviews, forum discussions, and surveys. Out of which, about 49 percent of people are active on social media. That’s roughly 59 percent of the world’s population. What’s the Relevance of Text Analytics in Today’s World?Īs of 2020, around 4.57 billion people have access to the internet. The results of text analytics can then be used with data visualization techniques for easier understanding and prompt decision making. For example, text analytics can be used to understand a negative spike in the customer experience or popularity of a product. Text analytics is used for deeper insights, like identifying a pattern or trend from the unstructured text. The term text mining is generally used to derive qualitative insights from unstructured text, while text analytics provides quantitative results.įor example, text mining can be used to identify if customers are satisfied with a product by analyzing their reviews and surveys. Text mining and text analytics are often used interchangeably. What’s the Difference Between Text Mining and Text Analytics? Text analytics uses a variety of techniques – sentiment analysis, topic modelling, named entity recognition, term frequency, and event extraction. ![]() It enables businesses, governments, researchers, and media to exploit the enormous content at their disposal for making crucial decisions. Text analytics combines a set of machine learning, statistical and linguistic techniques to process large volumes of unstructured text or text that does not have a predefined format, to derive insights and patterns. ![]()
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