A Note on Text Mining

Dec 17, 2021
4 min read

As individuals get more familiar with technology, all institutions are progressively migrating to electronic forms. Massive amounts of data are now being sent via the internet in the form of digital libraries, archives, and other textual information sources such as blogs, social media networks, and e-mails. Although it may appear to be more practical, organizing and dictating such a large quantity of data can be a time-consuming process. Even data mining technologies are incapable of managing unstructured textual data since it necessitates the investment of time and effort to extract information.

Natural Language Processing (NLP) is one of the most advanced machine learning technologies that is growing in every sector. Text mining is a subset of NLP that provides tools and strategies for delving into unstructured data to discover important patterns and insights. They are necessary for textual data analysis. This article is an introduction to text mining, in which we will learn about its processes, methods, and applications.

What is Text Mining?

Text mining is the technique of extracting important information from standard language text data. This is the information that we produce through text messages, papers, emails, and files written in common language text.

Text mining is generally used to extract useful insights or patterns from large amounts of data. Text mining is a collaborative topic that incorporates information retrieval, data mining, machine learning, statistics, and computational linguistics techniques. This is concerned with natural language text saved in semi-structured or unstructured forms.

Text mining seeks facts, connections, and confirmation from large amounts of unstructured textual data. This collected data is then transformed into a structured format that can be examined or shown immediately using HTML tables, mind maps, charts, and so on. Text mining uses a variety of techniques to process the text for this purpose.

The Process of Text Mining

The process of Text Mining is tedious and includes a lot of steps. We have simplified and presented the process below:

Text Preprocessing:

The first process under text mining is text preprocessing. It is used on a huge number of documents that contain unstructured and semi-structured data. It converts a raw text file into a well-described sequence of linguistically relevant units.

The following steps are performed under text preprocessing:

Text Cleanup performs a variety of functions, such as eliminating advertising from web pages and cutting out tables and figures, among others.
Tokenization is the process of converting sentences into words by removing spaces, commas, and other punctuation marks.
Filtering removes words with no important content information, such as articles, conjunctions, prepositions, and so on. Even terms that are often repeated are deleted.
Stemming is the act of converting words to their stems, or standardized forms, by creating fundamental forms of words to recognize them by their root word forms.
Lemmatization reorganizes the word to the correct linguistic root, which is the verb's base form. Throughout the procedure, the initial step is to comprehend the context, followed by determining the POS of a word in a phrase and finally identifying the 'lemma.'
Linguistic processing employs Part-of-Speech (POS), Word Sense Disambiguation (WSD), and Semantic structure.

Text Transformation:

Text transformation generates features after the feature selection procedure. Feature generation reflects texts based on the words they include and the occurrences of those words, where the order of the words is unimportant. It makes use of bag-of-words or vector space models.

In this context, feature selection refers to the process of selecting the subset of relevant characteristics that will be utilized in the creation of a model. It reduces dimensionality by removing redundant and unneeded elements.

Text Mining:

After the transformation, the text is mined through several techniques of mining like classification, clustering, summarization, etc.

Methods Used in Text Mining:

Numerous approaches are being developed to tackle text mining challenges; they are essentially appropriate information retrieval based on user requirements. Some typical approaches based on information retrieval techniques are as follows:

Term Based Method:

The term-based technique inspects the document based on terms and benefits from productive computing performance while capturing the theories for term weighting.

Phrase-Based Method:

Phrases are more ambiguous and contain more sorts of semantic information. Documents are expected based on phrases in this technique since they are less dubious and more convenient than individual terms. Some of the factors that limit performance are as follows:

As a result of the secondary analytical characteristics of words
A rare event
Extensive duplication and a lot of noise

Concept-Based Method:

This method concludes terms at the sentence and document levels; such text mining techniques are based on the analytic examination of words and phrases. An analytical analysis is used in this case to assess the word significance in the absence of a document.

Pattern Taxonomy Method:

Documents are evaluated using this approach based on patterns, which are generated in a taxonomy by applying a relation. Data mining techniques such as association rule, frequent itemset mining, sequential and closed pattern mining may be used to identify patterns.

Applications of Text Mining:

The following are some real-life applications of Text Mining:

Customer Service:

There are several options for a consumer to provide feedback, including chatbots, customer surveys, online reviews, support requests, and social network accounts. Combining feedback with text analytics technologies can result in rapid improvements in customer happiness and experience.

Risk Management:

Text mining can provide information about industry trends and financial markets in risk management by controlling sentiment data and extracting information from analyst reports and whitepapers.

Maintenance:

Text mining has the potential to automate decision-making processes by detecting patterns that correspond with issues, as well as preventative and reactive maintenance operations.

Healthcare:

In medical research, a manual inquiry is expensive and time-consuming, but text mining provides an automated alternative for extracting useful information from medical literature.

Spam Filtering:

Text mining provides a way for filtering and removing such emails from inboxes, improving user experience, and lowering the danger of cyber threats.

EndNote:

As technology progresses and changes at a rapid pace, data quantities, much of which is unstructured, continue to grow. This new deluge of big data necessitates that most businesses combine structured and unstructured data to provide extensive visibility and insights into their company and operations.

Text mining is a multidisciplinary topic that combines computational linguistics and natural language processing for extracting non-trivial information from unstructured textual input using text mining algorithms.  This article has taught us about text mining, its procedures, methodologies, and real-world applications.