{"id":1064,"date":"2015-03-25T17:35:48","date_gmt":"2015-03-26T00:35:48","guid":{"rendered":"https:\/\/blogs.ubc.ca\/coetoolbox\/?p=1064"},"modified":"2015-05-05T13:03:49","modified_gmt":"2015-05-05T20:03:49","slug":"lets-talk-about-text-analytics","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/coetoolbox\/2015\/03\/25\/lets-talk-about-text-analytics\/","title":{"rendered":"Let\u2019s talk about Text Analytics"},"content":{"rendered":"

\"\"Text analytics has been increasingly attracting\u00a0 the attention of the OR-analytics community as the amount of information stored as textual data increases. Just think about the amount of data stored in emails, news articles and social media, not to mention contact-center notes, surveys, feedback forms, and so forth. Some estimate that text analytics market will grow between 25% and 40% during the next 5 years.[1<\/a>][2<\/a>] But let\u2019s make sure we are talking about the same thing\u00a0when we talk about text analytics.<\/p>\n

So, what is text analytics? <\/p>\n

If defining analytics is a bit of a challenge<\/a>, defining text analytics is an even greater one. But, basically, text analytics transforms unstructured text into data that can be analyzed with traditional techniques to obtain business insights. [3<\/a>][4<\/a>]<\/p>\n

The distinctive characteristic of text analytics is that it works with unstructured text<\/a>. This kind of data does not have a predefined model of organization, resulting in irregularities and ambiguities that make it difficult to extract meaning from. (Although many times unstructured text can be found combined with structured data commonly found in records and surveys, such as dates, demographic information, etc.)<\/p>\n

The transformation of unstructured text into useful insights is done by applying methods from fields such as linguistics, statistics and computer science (most notably Natural Language Processing<\/a>). Some of these techniques are:<\/p>\n