Text Mining: the next Big Thing in Data Science

Data has been pivotal in human society ever since time immemorial. It has been written in the form of text since ages to keep the records that can be analyzed in the future. While the text was mainly captured in the physical forms back in time, with the advent of technology digital forms took its place. 

Data Science deals with the extracting and handling of the insights and knowledge that comes out of the recorded data (structured/ unstructured) for research and analysis purposes. While you might have heard of the Data mining before, but Text mining is a bit different from it.

Text mining that involves exploring, research and analysis of large forms of unstructured data with the help of software is the newly emerging area of Data Science which is the next Big Thing!

What is Text Mining?

Text mining is the process that is used for deriving high-quality information from text. This text is mostly an unstructured form of data that is obtained from various sources, for analysis. Various computing methods and algorithms are used to devise the patterns and trends using statistical pattern learning. These patterns and trends serve the insights that are used to curate the high-quality information out of the given text.

Why is Text Mining significant?

While curating the information from the structured data is easy and feasible, finding the relevant information from unstructured data often remains a humongous task that remains incomplete due to lack of resources. And thus, a section of the data is most of the times left out from being analyzed. 

Text mining is therefore important as it helps in fetching the information from an unstructured or semi-structured text that is stored in natural language using advanced analytics and statistical algorithms.

How is Text Mining performed?

There are various techniques that can be used in performing Text Mining, which includes categorization data, entity extraction, retrieval of information, clustering, visualization, machine learning, and the sentiment analysis that are used for extracting important information and key insights that are hidden in the given text content.

Steps to perform Text Mining

Here are the steps that are following to mine the information from any given unstructured text:

  • Collecting of unstructured data from the different sources that are allocated for the designated task. The data available at these sources might be in different formats such as web pages, plain text, pdf files, etc. This is done using Information Extraction technique.
  • Pre-process the collected data by performing cleansing operations so as to detect and remove any kind of anomalies that are present. This process is important as it tends to structure and classify the given unstructured data. Here the Information Retrieval and Clustering techniques are used for pre-processing.

At this step, the real essence of the text is restored by removing the stop words from stemming, so as to identify the root of the given word, and index the data accordingly. Thereby, classifying the data from being unstructured in nature to structured by using Text Summarization.

  • This data is further processed by applying controlling operations for auditing the data received after basic cleansing. Automatic processing is performed in this step. This is done using Natural Language Processing (NLP). 
  • Once the data is thoroughly processed to make it a rather structured one, patterns and trends are analyzed so as to get the information in the form of insights. Pattern analysis is done by using the Management Information System (MIS).
  • The information which is processed in the above steps is leveraged to extract valuable and relevant information that is used for decision making, research and analysis purposes at different instances. 

This information is duly stored in the databases and managed regularly to assist key applications that require text mining. 

Techniques that are used to perform Text Mining

Here are the techniques that are used in several instances in the above steps to perform Text Mining successfully:

  • Information Extraction

It is the process of extracting the meaningful text from large heaps of unstructured data in the form of text. Information Extraction emphasizes on identifying extracting of attributes, entities, and how they are interrelated to one another, from the given unstructured or semi-structured data that is given in the form of text.

  • Information Retrieval

It is a process that aims at extracting relevant patterns that are associated with one another, based on the set of phrases or particular words. This is done by using various algorithms that are used for tracking and monitoring the behavior of users so as to discover the data accordingly. 

Google and Yahoo also use Information Retrieval to understand the pattern of their users and give them a more personalized experience.

  • Categorization

It is a process that involves supervised learning using Natural Language Processing (NLP), where the texts written in normal language are assigned to various topics which are predefined, depending upon the context of their content.

In this process the text documents are gathered and processed and analyzed to find the right topics or indexes that are relevant for each of the documents. This co-referencing method is used in Natural Language Processing so as to extract the synonyms and abbreviations that are related to the given text. 

NLP is an automated process that is used for different sets of contexts that range from filtering the spam, personalized commercials that are being delivered or categorizing of the web pages using hierarchy, and a lot more.

  • Clustering 

It is a process that plays a key role in text mining. Clustering is used for identifying the intrinsic structures that are present in the text, and then organize them as clusters, which are the relevant sub-groups. These clusters can be used for further analysis making a lot more lean and specific. 

The main challenge that is faced while performing clustering is to make clusters that are significant and hold meaning from a text that is unlabeled, without any prior information about it.  

Clustering is considered as a standard mining tool, that is useful in data distribution, and often acts as a pre-processing step for other mining techniques.

  • Summarization 

It is a process that generates a compressed version of a particular text which is relevant and valuable for the end-user. It aims at browsing through various text sources and craft relevant summaries of the text, where a considerable amount of information is put in a concise format for better accessibility.

It involves various methods such as neural networks, swarm intelligence, decision trees, and regression models that are often combined together to generate the desired results.

Where is Text Mining used?

Text mining has already gained enough traction and is used across different industries in different capacities ranging from healthcare, business to the social media platforms. Here are some of the examples that highlight the applications of Text mining:

  • Customer Support

To enhance the overall customer support and enrich their experience, businesses have now started to invest in text mining techniques such as NLP. These access the text data from various sources that include customer calls, feedback, and surveys, with the aim to address the grievances that are faced by customers in a very short span of time with utmost efficiency. This improves customer engagement and opens new business avenues.

  • Social Media Analysis

Social Media is full of text data, which is often used to track various pieces of information that are published online. They are used to analyze the reaction of the target audience that you are engaging with on social media to help you get better insights.

  • Risk Management

In order to mitigate the underlying risks, and enabling efficient management of numerous sources that provides information in the form of humongous text data, finance industry uses text mining in their Risk Management Software. This lets them access the right information at the right time enhancing the investments.

  • Spam Filtering

Text mining helps in making your emails spam-free. Various methods are used to mitigate the spam which is the entry point for viruses and deteriorates the overall productivity by cluttering your inbox. Spam filtering saves resources and time spent unintentionally on spam communication.

  • Internet Security

Text analytics and intelligence help in mitigating the potential cyber security threats, with the help of anti-crime applications that remove unwanted content from the internet that could potentially harm the cybersecurity. 

Text mining is used by enterprises and security agencies to remove any kind of underlying risks.

  • Business Intelligence

Many companies are using text mining for analysis that supports their key decision making. Text mining makes the whole analysis process lot more efficient by cutting down the time spent on analysis with accurate insights. 

  • Contextual Advertising

The online advertising industry has been targeting the mass audience that is present on different online platforms, furthering the reach of the enterprises. Text mining makes it efficient by several folds by placing advertisements that are relevant laying a better impact.

  • Fraud Detection

Text mining has been helping in highlighting the fraud claims, helping the insurance companies at large. Companies can now process the claims at a much faster pace without falling for fraudulent claims with the help of text mining. 

On a closing note

With these applications, text mining has surely made a greater impact on various industries and is still in the work of extending its services to others. It wouldn’t be wrong to say that Text mining is the Next Big Thing in Data Science that would take the industry by storm.

Thus, leveraging this opportunity to learn the text mining as a part of advanced Data Science can help you establish a bright career as a Data Scientist. 

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