What are Text mining and Data mining and what are they for?

Did you know that, according to Domo's "Data never sleeps" report, every day, more than 2.5 trillion bytes of data? And this figure is only increasing.

Companies, both private and public, generate a multitude of data on a daily basis. This data is converted into useful information, which they can then use to make decisions.

For this purpose, techniques such as text mining or data mining are absolutely necessary. But, What are Text mining and Data mining and what are they for? 

Text mining: definition

Text mining is one of the fields of data science. This process involves the analysis of textual data of all kinds (from different media, languages, etc.) in order to understand them and establish a relationship between the different contents. This requires the use of statistical actions and/or search algorithms.

In this way, text mining allows trends to be discovered, identifying patterns in texts, such as keywords or the repetition of syntactic structures, among other things.

It is a very useful analytical tool, since it immense quantities of texts are studied and automatically. Consequently, text mining uses techniques acquired from machine learning or machine learning. machine learning.

Text mining was born in the 1980s with the aim of improve data processing, thus reducing human workload.. It should also be noted that text mining does not only apply to text files - such as a Word document - but goes much further: 

• Comments on social networks 
• User reviews
• E-mails
• Comments in blogs or forums
• Websites
• Surveys

Text mining phases

What are Text mining and Data mining and what are they for? In order to answer this question, it is important to know the different phases that make up this process of textual data analysis:

1. Compilation: This is the first phase of text mining and consists of collecting data from different sources of information. As mentioned before, it will be carried out in an automated way, although under the supervision of a data scientist. 
2. Pre-processing: It consists of identifying the content, extracting what is most representative of the text.
3. Cleanliness: Eliminate unnecessary or duplicate information.
4. Tokenisation: Translating texts' into entities or programming languages, in order to be recognised by the computer.
5. Discovery: O analysis of internal representations to determine established patterns.
6. Visualisation: Finally, the data sample will be usable to start working with.

What is Text mining for?

So what can we use Text mining for? A priori, it is one of the most widely used techniques by companies of all kinds. Through this methodology, it is possible to find out about a brand's target audience: find out their habits, tastes, what type of product they want...

These are the Text mining actionsThe courses are applicable to any work sector (biology, document management, medicine...), both in the public and private sectors: 

• Information extraction
• Classification of documents
• Generating reports
• Opinion mining analysis

What is data mining or data mining?

Do you know what "data mining" means? Here is its definition:

Data mining is the process of extracting important information from a large amount of data in order to generate a machine-understandable structure. All of this, with the aim of using this information, a posteriori.

Therefore, it is also necessary to use Artificial Intelligencemachine learning; as well as statistics or database systems.

Data mining is based on mathematical analysis, which, like text mining, establishes patterns and trends in the data.

Data mining applications

In the business world, there are 5 applications where Data Mining models can be visualised:

• Forecast: Data mining is used to predict the timing of sales.
• Risk and probability: Through emails, for example, the best potential customers are identified. In this way, the balance between risk and probability can be achieved. 
• Sequence search: Following the example of sales, the items that customers have purchased are analysed. This is done to predict future purchases.
• Classification: Group customers together, taking into account different elements they share with each other. In this way, actions can be predicted based on affinities.

Differences between Text mining and Data mining

In order to understand what text mining and data mining are and what they are for, it is necessary to understand that they are not the same thing; although these concepts are closely related.

Text mining, on the other hand, obtains information from this data in the form of text, unstructured informationData mining does start from a database, where the information is structured. Therefore, in the latter case, the search for information is easier.

Do you want to specialise in Data Science?

Have you already understood what Text mining and Data mining are and what they are for? Would you like to work in this technological field? At IMMUNE you have at your fingertips the following Data Science Masterwhich is also available online. Your Master Data Science Onlineyou will be able to study from wherever you want.  

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