Text Mining is one of the most critical ways of analyzing and processing unstructured data which forms nearly 80% of the world’s data. Today a majority of organizations and institutions gather and store massive amounts of data in data warehouses, and cloud platforms and this data continues to grow exponentially by the minute as new data comes pouring in from multiple sources.
As a result, it becomes a challenge for companies and organizations to store, process, and analyze vast amounts of textual data with traditional tools. Upskilling yourself with data science programs will help you overcome the challenges. Let’s talk more about text mining.
What is Text Mining?
According to Wikipedia, “Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text.” The definition strikes at the primary chord of text mining – to delve into unstructured data to extract meaningful patterns and insights required for exploring textual data sources.
Text mining incorporates and integrates the tools of information retrieval, data mining, machine learning, statistics, and computational linguistics, and hence, it is nothing short of a multidisciplinary field. Text mining deals with natural language texts either stored in semi-structured or unstructured formats.
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The five fundamental steps involved in text mining are:
- Gathering unstructured data from multiple data sources like plain text, web pages, pdf files, emails, and blogs, to name a few.
- Detect and remove anomalies from data by conducting pre-processing and cleansing operations. Data cleansing allows you to extract and retain the valuable information hidden within the data and to help identify the roots of specific words.
- For this, you get a number of text mining tools and text mining applications.
- Convert all the relevant information extracted from unstructured data into structured formats.
- Analyze the patterns within the data via the Management Information System (MIS).
- Store all the valuable information into a secure database to drive trend analysis and enhance the decision-making process of the organization.
Text Mining Techniques
Text mining techniques can be understood at the processes that go into mining the text and discovering insights from it. These text mining techniques generally employ different text mining tools and applications for their execution. Now, let us now look at the various text mining techniques:
Let us now look at the most famous techniques used in text mining techniques:
1. Information Extraction
This is the most famous text mining technique. Information exchange refers to the process of extracting meaningful information from vast chunks of textual data. This text mining technique focuses on identifying the extraction of entities, attributes, and their relationships from semi-structured or unstructured texts. Whatever information is extracted is then stored in a database for future access and retrieval. The efficacy and relevancy of the outcomes are checked and evaluated using precision and recall processes.
2. Information Retrieval
Information Retrieval (IR) refers to the process of extracting relevant and associated patterns based on a specific set of words or phrases. In this text mining technique, IR systems make use of different algorithms to track and monitor user behaviors and discover relevant data accordingly. Google and Yahoo search engines are the two most renowned IR systems.
This is one of those text mining techniques that is a form of “supervised” learning wherein normal language texts are assigned to a predefined set of topics depending upon their content. Thus, categorization or rather Natural Language Processing (NLP) is a process of gathering text documents and processing and analyzing them to uncover the right topics or indexes for each document. The co-referencing method is commonly used as a part of NLP to extract relevant synonyms and abbreviations from textual data. Today, NLP has become an automated process used in a host of contexts ranging from personalized commercials delivery to spam filtering and categorizing web pages under hierarchical definitions, and much more.
Clustering is one of the most crucial text mining techniques. It seeks to identify intrinsic structures in textual information and organize them into relevant subgroups or ‘clusters’ for further analysis. A significant challenge in the clustering process is to form meaningful clusters from the unlabeled textual data without having any prior information on them. Cluster analysis is a standard text mining tool that assists in data distribution or acts as a pre-processing step for other text mining algorithms running on detected clusters.
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Text summarisation refers to the process of automatically generating a compressed version of a specific text that holds valuable information for the end-user. The aim of this text mining technique is to browse through multiple text sources to craft summaries of texts containing a considerable proportion of information in a concise format, keeping the overall meaning and intent of the original documents essentially the same. Text summarisation integrates and combines the various methods that employ text categorization like decision trees, neural networks, regression models, and swarm intelligence.
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Applications Of Text Mining
Text mining techniques and text mining tools are rapidly penetrating the industry, right from academia and healthcare to businesses and social media platforms. This is giving rise to a number of text mining applications. Here are a few text mining applications used across the globe today:
1. Risk Management
One of the primary causes of failure in the business sector is the lack of proper or insufficient risk analysis. Adopting and integrating risk management software powered by text mining technologies such as SAS Text Miner can help businesses to stay updated with all the current trends in the business market and boost their abilities to mitigate potential risks. Since text mining tools and technologies can gather relevant information from across thousands of text data sources and create links between the extracted insights, it allows companies to access the right information at the right moment, thereby enhancing the entire risk management process.
2. Customer Care Service
Text mining techniques, particularly NLP, are finding increasing importance in the field of customer care. Companies are investing in text analytics software to enhance their overall customer experience by accessing the textual data from varied sources such as surveys, customer feedback, and customer calls, etc. Text analysis aims to reduce the response time of the company and help address the grievances of the customers speedily and efficiently.
3. Fraud Detection
Text analytics backed by text mining techniques provides a tremendous opportunity for domains that gather a majority of data in the text format. Insurance and finance companies are harnessing this opportunity. By combining the outcomes of text analyses with relevant structured data these companies are now able to process claims swiftly as well as to detect and prevent frauds.
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4. Business Intelligence
Organizations and business firms have started to leverage text mining techniques as part of their business intelligence. Apart from providing profound insights into customer behavior and trends, text mining techniques also help companies to analyze the strengths and weaknesses of their rivals, thus, giving them a competitive advantage in the market. Text mining tools such as Cogito Intelligence Platform and IBM text analytics provide insights on the performance of marketing strategies, latest customer and market trends, and so on.
5. Social Media Analysis
There are many text mining tools designed exclusively for analyzing the performance of social media platforms. These help to track and interpret the texts generated online from the news, blogs, emails, etc. Furthermore, text mining tools can efficiently analyze the number of posts, likes, and followers of your brand on social media, thereby allowing you to understand the reaction of people who are interacting with your brand and online content. The analysis will enable you to understand ‘what’s hot and what’s not’ for your target audience.
We hope this informative piece helped you understand the basic of text mining and its applications in the industry. If you are interested to know more about data science techniques, check out Executive PG Programme in Data Science from IIIT Bangalore.
What are the benefits of text mining?
Text mining is the process of analyzing huge collections of documents in order to find new information or to assist in the answering of specific research questions. Text mining uncovers facts, connections, and claims that would otherwise be lost in a sea of textual data. Text mining can assist in the tracking and interpretation of text created by emails, news, and blogs. Companies may use text mining technologies to assess their brand's visibility, posts, likes, and followers. This provides organizations with a clear picture of how their customers react to their brand and content. There are also a slew of open-source tools that make conducting some basic text mining a breeze.
What are the most significant problems with text mining?
Textual data presents additional problems, such as erroneous spelling and sentence structure, which makes it difficult to extract the relevant information and analyze it. During the text mining process, important difficulties and obstacles such as domain knowledge integration, variable concept granularity, multilingual text refining, and natural language processing ambiguity occur. Synonyms and antonyms are all used in texts, which causes issues for text mining techniques that take both into account. When a collection of documents is vast and comes from several disciplines in the same domain, categorizing them might be challenging.
How can text mining tools make your job easier?
Text mining technologies are used to analyze various forms of text, ranging from survey answers and emails to tweets and product reviews, in order to assist organizations in gaining insights and making data-driven choices. The good news is that there are several online resources and tools available to assist you in getting started with text mining. However, many organizations are faced with the decision of whether to create or acquire text mining software. If you know how to code, you can create your own text mining models using open-source tools. If you don't have the time or resources, there are many cost-effective, accurate, and dependable online tools available.