The vastness of text data and how to deal with it

Published by Analyttica

Standing in 2022, the realm of Data Science is unparallel, with almost everything jotted down as text data. It ranges from regular text messages to emails, tweets, online reviews, documentation, files, and survey reports, along with many others. Oddly enough, these texts, when stored in a computing device, are in the form of unstructured data. Going through these enormous collections of words and characters and drawing meaningful insights and patterns is called text data mining.

Importance of mining text data

With the ever-increasing technological advances, the text mining market has grown exponentially over the past couple of years. It has become quite the achievement in the highly competitive business market. But why is that so?

The reason behind this is quite simple. It is next to impossible to manually analyze the vast volume of unstructured data captured daily in an IT environment. This, in turn, has led to the demand for text mining and analytics. It offers insights by converting the unformed text into consumable and actionable structured data that can help derive underlying patterns and themes.

Process of text data mining

Extracting the highest quality of information from a set of unstructured data is no easy feat. Here are some of the major steps for achieving the same:

  1. Text pre-processing

Text pre-processing is often considered the first step in Natural Language Processing (NLP). It refers to transforming and cleansing the text so that it becomes predictable and analyzable. This is done in a series of steps noted below:

  • Text data cleansing: As the name suggests, data cleansing or cleaning denotes detecting and removing inaccurate information from the database. It includes noise removal that is present in the form of ads from web pages, tables, figures, and formulas. It also focuses on normalizing texts that are converted from binary formats.
  • Tokenization: Next is tokenization which refers to splitting the entire text into smaller tokens or units. These can include dealing with words, symbols, characters, apostrophes, hyphens, and even numbers. Tokenization is usually done based on the whitespace between two consecutive words.
  • Parts-of-speech tagging: It is hardly a secret that a word can denote different things based on the context it is used in. This makes part-of-speech tagging a prerequisite in text pre-processing. It refers to identifying the grammatical connotation of a word or its corresponding parts of speech. In any text, there can be multiple POS tags where the goal is to ensure the right tag is allocated to the right context.


  1. Text transformation

The next significant step to take into consideration is the transformation of texts. This adds up to the capitalization, segregation, and representation of texts under two main segments:

  • Bag of words: Simply put, this is a way of categorizing words from a linguistic perspective. It indicates the number of times a word has been used or occurred within a document. Here, each word is marked as a separate variable with a numeric weight. This model does not care for the order or structure of words in the document, only their count.
  • Vector space: This analyzes the text from a statistical perspective. It uses frequencies for information retrieval and is therefore termed vectorizing text. The vector space focuses on essential terms and how many documents contain them. In most cases, the term document matrix is used, a mathematical matrix with columns dedicated to terms/words and rows used as a document vector.


  1. Feature selection

Once the text document is analyzed and represented using either of the two approaches mentioned above, it is time to move on to feature selection. Note that when it comes to addressing tasks, a high dimensionality can be a source of difficulty for learners. Thus, this stage requires selecting a subset of the words, features, or input variables. The main purpose of this is to get rid of terms that provide no predictive information and may be misleading or redundant, like stop words. Thereby reducing the costs of data analysis in the process. The two major approaches here are:

  • Select before use: This means selecting and evaluating features before introducing them to a classifier.
  • Select based on use: This denotes evaluating features based on their performance in a classifier or actual use.


  1. Pattern discovery

Once the attributes are selected and the irrelevant features removed, next is pattern discovery. It refers to the technique of pinpointing patterns from the document. This is done via the pattern taxonomy model, which focuses on extracting and updating the discovered patterns for carrying out knowledge discovery.

  1. Interpretation and Evaluation

As the name suggests, mining quite literally denotes the process of segregating what is useful from what is not. The same rule applies to text data mining. Here, through interpretation and evaluation, the data that is gathered is either terminated or iterated based on whether or not they are well-suited for the application at hand.


Mining text data is hardly regarded in the highest esteem when it comes to real-world data cases. Until recently, it was neither spoken of nor added in the introductory Data Science courses.

Nevertheless, times are changing along with people’s approach to this time-consuming task. Not just that, LEAPS by Analyttica has understood and acknowledged the increasing importance of mining text data. It has developed an experimental hands-on approach that will allow enterprises to train their talents along the lines of cleansing, treatment, structural representation, and visualization of text data. Thus, making it the perfect go-to option for extracting and managing knowledge from vast text data.