Text mining methods and techniques: The beginner’s guide

Data is growing at an exponential rate. A large amount of text data is flowing over the Internet daily in the form of news, blogs, email, social media, digital libraries, repositories, etc.

Because of the sheer volume, it is almost impossible to extract valuable knowledge out of the data in a short time and use it for our benefit.

The solution is to utilize automated data extraction or text mining procedure to explore, retrieve, and analyze valuable information. An extension of data mining, text mining, in a nutshell, obtains information, patterns, and trends from a large amount of free format textual data for a specific purpose.

What is text mining?

We have already defined what text mining is. For academic purpose, let’s try again. It is a multi-disciplinary field based on information retrieval, data mining, machine learning, statistics, and computational linguistics. Unlike data stored in databases, the text is unstructured, ambiguous, and challenging to process. Text mining applies several text mining techniques like summarization, classification, and clustering to extract knowledge from natural language text, which is stored in a semi-structured and unstructured format.

Text mining techniques are continuously used in areas like search engines, customer relationship management systems, filter emails, product suggestion analysis, fraud detection, and social media analytics for opinion mining, feature extraction, sentiment, predictive, and trend analysis.

In general, text mining uses four different methods:

1. Term-based Method

It is a method when a document is analyzed based on a term that it contains. The term may have some value or meaning in a context. Each term is associated with a value, known as weight. This method, however, has two problems: 1. Polysemy (a term having many possible meanings), and 2. Synonymy (multiple words having the same meanings.)

2. Phrase-based Method

As the name indicates, this method analyses a document based on phrases which carry more information than a single term, because they are a collection of semantic terms. They are more descriptive and less ambiguous than a term. But this method isn’t devoid of any problems. The performance of this method could vary due to three reasons:

  1. Inferior statistical properties to terms
  2. Low frequency of occurrences.
  3. Redundant phrases and noisy phrases.

3. Concept-based Method

In the concept-based method, the terms are predicted or guessed at a sentence or a document level. Rather than a single term analysis, this model tries to analyses a term on a document or sentence level by finding a significant matching term aptly. This model contains three components:

  1. Examining the semantic construction of sentences.
  2. Building a conceptual ontological graph to describe the semantic structures.
  3. Extracting top concepts based on the first two components to build feature vectors using the standard vector space model.

4. Pattern Taxonomy Method

In the pattern-based model, a document is analyzed based on a pattern i.e., a relation between terms to form taxonomy, which is a tree-like structure. The pattern-based approach can improve the accuracy of the system for evaluating term weights because discovered patterns are more specific than whole documents.

Patterns can be discovered by using data mining techniques like closed pattern mining, sequential pattern mining, frequent itemset mining, and association rule mining. The pattern-based technique uses two processes pattern deploying (PDM) and pattern evolving. This technique refines the discovered patterns in text documents.

All text mining process follows these steps:

  • Collecting information: The textual data from various sources that are in a semi-structured or unstructured format is collected to perform text mining.
  • Conversion into structured data: Pre-processing involves cleaning the data that is collected.
  • Pattern identification: Various techniques used in text mining, which are discussed later, are then applied to extract meaningful information.
  • Pattern Analysis: The data obtained is analyzed to extract knowledge and meaning out of it.
  • Advanced analysis: Finally, the required knowledge is obtained and can then be used for further analysis

There are several text mining tasks performed while analyzing the text. They are:

  • Clustering
  • Factor analysis
  • Text classification
  • Text purification
  • Text summarization
  • Distributed storage and retrieval
  • Find similar documents
  • Find an association between terms
  • Find commonly occurring terms.

Popular text mining techniques

1. Information Extraction (IE)

Information extraction (IE) is a technique to automatically extract a piece of definite, structured information from unstructured or semi-structured data in the form of text using Natural Language Processing. It is used for the extraction of entities from the text, like names of persons, organization, location, and the relationship between entities, attributes, events, and relationships.

The extracted information is well-organized (structured) and stored in a database for further use. IE extracts specific attributes and entities from the document and establishes their relationship. The process used to check and evaluate the relevance of results is called ‘Precision and Recall.’

2. Information Retrieval (IR)

Information retrieval (IR) refers to finding and collecting relevant information from a variety of resources, usually documented in an unstructured format. It is a set of methods or approaches for methodically developing information needs of the users in the form of queries that are used to fetch a document from a collection of databases. IR helps to extract relevant and associated patterns according to a given set of words or phrases.

3. Text Categorization

This technique involves designating pre-decided categories to free-text documents that contain insights about the world. The purpose of text classification/text categorization is to increase the detection of information that can lead to a better decision. For example, news stories are typically organized by subject categories (topics) or geographical codes. Technical domains and subdomains often classify academic papers.

At the same time, patient reports in healthcare organizations are often indexed from multiple aspects, using taxonomies of disease categories, types of surgical procedures, insurance reimbursement codes, and so on. Another widespread application of text categorization is spam filtering, where email messages are classified into the two categories of spam and non-spam, respectively.

4. Document Clustering

This technique is used to find groups of documents with similar content. It makes use of descriptors and descriptor extraction that are essentially sets of words that describe the contents within the cluster. It is an unsupervised process responsible for classifying objects into groups called clusters, which consist of several documents. Dividing similar text into the same cluster forms the basis of this method.

Any labels associated with objects are obtained solely from the data. The advantage of this technique is that it ensures that no document is missed from search results since documents can emerge in numerous subtopics. For example, if clustering is performed on a collection of news articles, it can make sure that similar documents are kept closer to each other or lie in the same cluster.

5. Text Visualization

Text Visualization is a technique that represents large textual information into a visual map layout, which provides enhanced browsing capabilities along with simple searching. In text mining, visualization methods can improve and simplify the discovery of relevant information.

Text flags are used to show the document category to represent individual documents or groups of documents, and colors are used to show density. Visual text mining puts large textual sources in an appropriate visual hierarchy, which helps the user to interact with the document by scaling and zooming.


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