Text mining is also known as text data mining because it has a lot in common with traditional data mining. While data mining collects information in a computer-rendered form, text mining analyzes unstructured data in plain text form in order to classify the information in multiple documents. It prevents the need to comb through large amounts of information, helping to determine how a company is talked about in the media, helping search engines create more relevant search results pages, and more.
Text mining refers to the process of analyzing large amounts of unstructured text data. Specialized software scans massive amounts of text, looking for concepts, patterns, topics, keywords, and many other features that can be controlled by the team doing the mining.
It is more important than ever today because there are massive amounts of text data that need to be analyzed. A specialized program can do it much faster than a human being, and with the development of big data platforms and deep learning algorithms, more can be deduced accurately from the text than in the past.
Text mining and data mining are similar. However, the former focuses on text instead of other forms of data.
In order for it to be useful, the text does need to be organized first. It must be categorized, clustered, and tagged. The process also involves the use of natural language processing technology. It allows users to more effectively interpret data sets by applying computational linguistics to the process.
Deep learning models require less direction than more traditional software. They use neural networks to analyze data in a flexible, intuitive way that is difficult for conventional machine learning to duplicate.
For example, a deep learning model could review the content in multiple documents and separate them based on various topics, without the direct input from an analyst.
There are many ways text mining is used. It can be used by companies in their reputation management efforts. Mining efforts could be used to scan text online to uncover how the company is being discussed in the media, without the need for individual people to scour the internet and read multiple articles. This is sometimes referred to as opinion mining, and it can include information from online reviews, social media, and more.
Text mining is an effective way to screen job candidates. Human resource departments can screen resumes according to keywords to narrow in on just a few applicants.
Mining programs can block spam emails by looking for keywords and phrases, and website content can easily be categorized and classified. The insurance industry can easily find fraudulent claims, and the medical field can analyze descriptions of medical symptoms to find the best diagnoses for a patient.
It is often used by search engines, like Google, to better understand the content on web pages so search queries can be optimized. That’s why the use of keywords is popular among content creators. It’s easier for mining programs to find certain keywords than broader ideas hidden within a sentence.
Text mining is a more efficient way to comb through massive amounts of text. By analyzing text in this way, companies can detect various problems before they become huge issues. It has the ability to detect customer turnover rates while keeping on top of fraud detection, risk management, and boost online advertising.
It also poses some challenges. Data can be vague, inconsistent, and contradictory, which can make it hard for a skilled program to determine the type of content and classify it properly. Syntax and semantics can also cause problems, as can texts that are translated from different languages. In these cases, the attention of an analyst is important to ensure the program is performing appropriately.
In addition, text mining can require a lot of processing power. Running a session can be expensive, and it can compromise other business activities.