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Text Mining from Data Warehouse

The commonly known definition of a warehouse is a place to store things. These things could include food, electronic supplies, transportation material, or any goods that need to be stored and utilized in the future. Similarly, data regarding any organization, individual, or place is also valuable and needs to be stored. Therefore, the companies build a data warehouse to store such essential data.

Text Mining from Data Warehouse

What is a Data Warehouse?

An organization usually stores data in various forms. Mostly, they keep it on sheets of paper, excel files, CSV files, databases, and other storage ways. In the language of computer science, scientists consider a data warehouse as a massive collection of data regarding any business. The data warehouse is also known as enterprise data. It is an essential component of business intelligence. Business data of organizations are usually in the form of multiple vast files of the collection of databases. The companies collect this data from various sources and put it together at a single place for better comprehension to make decisions for the business in upcoming times. Therefore, a data warehouse is an essential part of the organization to work better by analyzing previous performances.

What is Text Mining?

Text mining is a technique of extracting useful information from unstructured or unorganized data. It is also known as text data mining and is quite similar to text analysis in terms of its usage. Text mining means to use computations to process valuable information from the available text and extracting that for future reference. The organizations usually gather the required data from different sources for processing and extraction. These sources are commonly known platforms such as social media, websites, emails, books, and articles. Then they put the gained information or text through analysis and observe the clear patterns regarding that information. Finally, they use these statistical patterns and trends to form essential decisions regarding business data.

Subtypes of Text Mining

Text mining helps in data processing and highlights prioritized information from text. It does so through various tasks. Some of them are the following:

  • Text categorization
  • Text clustering
  • Document summarization
  • Sentiment analysis
  • Entity extraction

Purpose of Text Mining

There is massive competition in the business market with the growth of awareness among people about analytics. Text mining provides the opportunity to extract valuable information from scattered data on various platforms and visualize the latest market trends. Moreover, it helps in efficient decision-making by using previously available information to provide future benefits.

How Text Mining Works?

Text mining uses analytical tools for the processing and extraction of informative data. Following are some of the domains in which text mining works:

  • Information Extraction
  • Natural Language Processing
  • Data Mining
  • Information Retrieval

Text mining is a complete process that takes data as input and applies essential strategies to put it through extraction and cleansing to get valuable information at the end of the process. The complete process of extracting efficient information through text mining is the following:

1. Text Pre-processing

In this step, the available information goes through pre-processing, meaning that irrelevant or unnecessary information is filtered out here. They then pass the rest of the relevant information by splitting into white spaces using vector space representation. Moreover, mapping the unknown and ambiguous words is done by part of speech tagging.

2. Text Transformation

After extracting relevant data from the original unstructured data, they represent the remaining data using two main representation approaches to control the text capitalization. The two mentioned techniques of representation are:

a. Bag of words (bow)

The bag of words model is a feature extraction model from the text and text documents. It represents textual data in the form of bags, which disregards the blank spaces, and gives the information about known words in a certain document.

b. Vector Space

The vector space model is the representation of the words in the multidimensional vector space.  The modeling algorithm in this model places similar objects close to each other and vice versa. In other words, the similarity or differences between the objects determines the distance between them.

3. Feature selection

This step is crucial in text mining. The proper selection of features leads to efficient results produced at the end of the process. Relevant and essential elements are selected for the creation of the model later. Irrelevant or repeated selection of features leads to the wastage of resources and time and produces results that are not productive. This step is also known as variable selection or attribute selection.

4. Data Mining

In this step, they merge data mining procedures and conventional procedures. Moreover, classic data mining procedures such as Classification, Association, Clustering, Regression, and Prediction, are used in the now formed structured database.

5. Evaluation

After going through all the procedures mentioned earlier, step by step, the results formed from processing and analyzing the information are adequately gone through the evaluation process. Finally, they discard the results after the evaluation.

6. Applications

Over time, text mining has proven to be an essential procedure for business growth analysis and forming business plans. It helps with the business’s growth by understanding the outcomes of previously used strategies and provides a broader look at the horizon of success. Therefore, it has become an integral part of every organization, and its applications are numerous. Some of the applications of data mining are the following:

  • Risk Management
  • Knowledge Management
  • Customer Care Service
  • Business Intelligence
  • Content Enrichment
  • Fraud Detection
  • Cybercrime Prevention
  • Contextual Advertising
  • Social Media analysis
  • Web Mining
  • Resume Filtering
  • Medical Advancements
  • Spam Filtering

In today’s data-driven world, where the data and analytics are defining the basis of competition in the markets and among the companies, text-mining has become very popular. The companies use this technique to extract useful information from the gathered data and use that extracted information to make critical business decisions to stay ahead of the competition.

Moreover, the more information the companies have about their clients, the more they can personalize the user experience and understand their needs. Additionally, with the digitization of systems and easy access, the criminals have also shifted to cyberspace for their heinous activities.

However, text mining’s predictive ability is coming in very handy for the law-enforcing departments to classify and identify the potential threats and activities. Although the companies are invading the users’ privacy to get the data for this process, which is a massive concern as everyone puts a considerable amount of personal information on the internet.

However, it is also benefitting the user in terms of their internet experience and the relationship with the companies. Overall, text mining has many benefits for the users and clients both; however, the companies must do it without invading the users’ privacy rights.


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