Difference Between Text Mining and Data Mining

We live in a digital era where massive amounts of data are collected daily. Terabytes or petabytes of data are generated every twenty-four hour period. But, the data in its raw form is of no use, so analyzing such data is important. Data mining helps analyze such massive volumes of data past providing tools to detect knowledge from data. Text mining is a sub-type of data mining that turns untapped text data into valuable resources.

What is Information Mining?

Similar to how gold ore is extracted from the earth in its pure grade through mining, data mining is the sorting and extraction of meaningful data or data from big datasets. Information mining typically involves identifying trends or patterns in information that usually go beyond unproblematic analysis procedures using software algorithms and statistical methods. Also known as knowledge discovery in information (KDD), data mining seeks to obtain valuable information from data in order to assistance reply business questions and predict future trends and beliefs.

Information technology tin be viewed as a result of the natural evolution of data technology. Simply put, data mining is cognition mining from information. The data sources can include databases, information warehouses, the World wide web, or other information repositories. It tin be applied to basically all forms of data including spatial data, graph or networked data, information streams, ordered/sequence information, and text data.

What is Text Mining?

Text mining, also called text data mining, is the procedure of extracting meaningful insights or data from unstructured text data. Information technology is a sub-type of information mining that involves text – one of the nigh common data types within databases. Similar to data mining, information technology seeks to extract useful data from data sources by identifying and exploring patterns in data. In text mining, however, the data sources are restricted to text. It filters big amounts of text data and extracts the relevant you need.

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Text mining requires structuring the input text followed by identifying patterns within the structured data, and evaluation and interpretation of the output.  A key element of text mining is document collection, which involves group of text-based documents. Typically, text mining involves keyword extraction, nomenclature and clustering, document summarization, anomaly and tendency detection, and text streams.

Difference betwixt Text Mining and Information Mining


 – Data mining is the automated processing of collecting and analyzing large amounts of data sources in order to observe meaningful insights or observe subconscious patterns from data in a way that provide some valuable information. Data mining simply means knowledge mining from information. Text mining is a office of data mining that seeks to extract useful information from information sources by identifying and exploring patterns in text-based information. Text mining is the processing of text information from documents.

Data Sources

 – The different sources of data used in the process of data mining include data warehouses, the World Wide Web, transactional databases, multimedia databases, spatial databases, flat files, and other information repositories. The widely used data sources for text mining include data from sources like social media, emails, letters, product reviews, forums, news articles, library databases, web scraping, and then on.

Mining Methods

 – The most important data mining techniques are data collection and cleaning, information preparation, tracking patterns, classification, association, anomaly detection, clustering analysis, regression analysis, and prediction. Some of the most common text mining techniques are information retrieval, text categorization, classification and clustering, certificate summarization, sentiment analysis, bibelot and trend detection, and text streams.

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Text Mining vs. Information Mining: Comparison Chart


Information mining ways sorting and extraction of meaningful information or data from big datasets for the purpose of cognition discovery. There are many terms with a similar meaning, for example, noesis mining from data, knowledge discovery, knowledge extraction, information/pattern analysis, and and then on. It involves identifying trends or patterns in data that commonly go beyond simple analysis procedures using software algorithms and statistical methods. Text mining, on the other hand, is built on diverse information mining approaches to place trends in data, except in text mining, information assay relies on certificate collection. It makes use of background knowledge to a much greater extent than data mining.

What is text mining with examples?

Text mining is identifying hidden patterns in untapped text information and turning those data sources into actionable insights. Examples of text mining include customer surveys, online reviews, risk management, business intelligence, fraud detection, etc.

What is the deviation between text mining and NLP?

While both concur the cardinal to unlocking the business value inside the large datasets, NLP is focused on making computers understand human behavior through text, voice communication, sentiment, and actions. Text mining is simply extracting meaningful insights or information from unstructured text information.

Is NLP a information mining?

NLP is a component of text mining that helps computers to process and analyze large amounts of natural text data. Information technology seeks to extract information from text, like text mining. NLP and data mining are both essential elements in information scientific discipline.

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What are the comparison between information mining text mining and web mining?

Data mining is a collective term for both text mining and spider web mining. Data mining just means knowledge mining from data; text mining is extracting meaningful insights or information from unstructured text information; and spider web mining is to use data mining techniques to discover hidden patterns from the Www.

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