{"id":1304,"date":"2019-08-02T09:46:14","date_gmt":"2019-08-02T09:46:14","guid":{"rendered":"https:\/\/www.revuze.it\/?p=1304"},"modified":"2019-08-02T09:46:14","modified_gmt":"2019-08-02T09:46:14","slug":"text-analytics","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/text-analytics\/","title":{"rendered":"What Is Text Analytics?"},"content":{"rendered":"
Text analytics is an <\/span>automated process to analyze a piece of writing and extract useful information<\/b> from it. It is often carried out with the help of software designed to go through lengthy texts and gather insights that may be useful for marketing, branding, and other research purposes.<\/span><\/p>\n Many companies use text analytics to analyze articles, tweets, social media posts, reviews, comments, and other types of writing, to find meaning and gather intelligence with the help of algorithms and machine learning<\/a> tools.<\/span><\/p>\n In this article, we\u2019ll dive into the world of text analytics, we\u2019ll explore its different applications, and we\u2019ll learn about the different steps of this innovative process.<\/span><\/p>\n If you are vaguely familiar with the concept of text analytics, then it\u2019s likely you\u2019ve also heard the terms \u201ctext mining\u201d and \u201cnatural language processing.\u201d Are these the same, or do they refer to different tools and processes?<\/span><\/p>\n Text mining is very often used as a synonym of text analytics, so these two terms mostly refer to the same concept. However, text mining is a broader term that refers to the act of gathering useful, high-quality information from a text.<\/span><\/p>\n Instead, text analytics is the more specific <\/span>computational process<\/b> of analyzing a text to extract such information. Text analytics softwares use linguistic, statistical, and machine learning techniques to structure the content of a text and analyze it.<\/span><\/p>\n In order to perform such analysis, programs use natural language processing (commonly abbreviated as NLP), which is the <\/span>field of computer science, information engineering, and artificial intelligence used to process and analyze the data contained in a piece of writing<\/b>. NLP is used to understand the meaning behind a text. It usually tries to answer the following questions about a piece of writing: Who\u2019s talking? What are they saying? What are they referring to?<\/span><\/p>\n In general, text analytics softwares apply both text mining and natural language processing.<\/span><\/p>\n So what is text analytics actually used for?\u00a0<\/span><\/p>\n Text analytics is used in many different fields, from science to academia, from security to biomedicine. In particular, it\u2019s used for business and marketing applications to perform research on markets and customers, make business decisions, and predict future behaviors.<\/span><\/p>\n Here are some examples of useful applications:<\/span><\/p>\n Some companies gather thousands of words in <\/span>customer feedback<\/b> every single week. Just think of Yelp reviews, customer surveys, Facebook comments, and other forms of feedback. If they don\u2019t have the automated tools required to process and analyze the feedback to extract information that can be used to improve the product, customer service, or the branding strategy, what\u2019s the point of even getting the feedback in the first place? Text analytics can help you transform those texts into precious insights.<\/span><\/p>\n Anyone who works in communications, marketing, or branding knows that social media (i.e. Facebook, Twitter, Reddit, etc.) can be the greatest resource to discover customers\u2019 opinions and feelings about businesses, products, and services. Text analytics is an indispensable tool to analyze whatever people write and share online that mentions your brand or your competitors\u2019 brands. As always, one main rule applies: there is no point of having all this informational available, if you don\u2019t know how to use it. It\u2019s important to note that the writing style on social media can be quite specific and different from other types of writing; sentences can be very short, words can be abbreviated, and emojis often substitute words to express feelings and opinions. A good NPL software needs to be able to understand all of that.<\/span><\/p>\nThe difference between text analytics, text mining, and NPL<\/b><\/h2>\n
Functions and applications of text analytics<\/b><\/h2>\n
Voice of customer and customer experience<\/b><\/h2>\n
Social media monitoring<\/b><\/h3>\n
Sentiment analysis<\/b><\/h3>\n