Today, we have an abundance of information available to us on the internet. But, most of it is contained in the form of unstructured text. Enterprises that hold this data find it difficult to store, process, and analyze it. Similarly, retrieving useful information from such unstructured data sources, too, is a hassle. This difficulty in finding only the relevant information may prove critical in certain sectors, such as healthcare and finance. This is where text mining comes to our rescue.
Text mining refers to the process of extracting high-quality information from unstructured data, quickly. It also ensures that the unstructured data can be managed easily, making it accessible and useful for businesses and customers alike. Text mining can be used in various industries for streamlining processes and improving their efficiencies. Some of the text mining applications across multiple sectors are discussed below –
How these five text mining applications can help in various business operations
1. Servicing customers
One of the beneficial text mining applications is its use in customer care services. We are all aware of the difficulties faced by B2C enterprises in providing high-quality service to their customers. Customer care representatives are always bombarded with tons of requests and queries which can become difficult to handle.
This over-influx of data can lead to a degradation in the quality of customer care services provided. It can lead to damage to brand reputation and drive away customers. But, with text mining, enterprises can improve their customer care services significantly.
With natural language processing capabilities of a text analytics software, enterprises can easily analyze textual data collected from customers in the form of surveys, complaint tickets, and other sources. The analytics software can then send an automated response to the customer based on their queries and complaints. This helps reduce the work burden on employees. This can lead to enterprises improving their quality of service, speed, and effectiveness in solving customers’ issues.
Text mining Python is also getting a lot of attention these days. By generating textual data from many sources, such as surveys, user comments, and user calls, etc., businesses are investing in text analytics tools to enhance their overall customer experience.
2. Contextual digital advertising
Digital marketing, in a way, has eclipsed traditional marketing practices. But, digital marketing is not a child’s play. When it comes to having web ads, the failure or success depends upon what ads are run and where they are displayed.
Enterprises might have the best marketing campaign with eye-catchy ads, but, if they are not displayed to the correct end-user, they may end up being of no value. This is where text mining applications and tools step in. With text mining, enterprises can run contextual web ad campaigns that bring them a high ROI. By understanding the context on a webpage with the help of text mining software, they can place ads that are relevant to the information contained in the webpage.
This increases the chance of the click-through rate for the ads and leading to a sale, as users will be more likely to click on an ad showing a similar product or providing related information to the subject they are already reading on. For instance, an advertisement for a refrigerator will perform better on a web page talking about home appliances, rather than a webpage talking about baby food.
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3. Preventing cyber crimes
Unfortunately, the rise of internet use has also increased the instances of cyber crimes such as phishing and cyberbullying, to name a few. A cyber-security app with text mining capabilities can help detect hidden information, such as malicious code or scripts, in unstructured messages. This can help reduce instances of financial cybercrimes, such as phishing. Similarly, text mining applications can also help detect words that are commonly used for bullying, threatening, or other harmful activities on the internet.
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Law enforcement agencies or other responsible enterprises can ensure that instances of cyberbullying are reduced by monitoring content containing such words by employing text mining software.
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4. Detecting insurance frauds
Insurance companies usually face instances of false insurance claims. The entire process of insurance claim is dependent on unstructured data, in the form of customer details, cause of insurance claim, etc. It becomes difficult for enterprises to manage such large volumes of data, process claims quickly, and also ensure that the claim filed by the customer is genuine.
With text mining applications, enterprises can manage and analyze customer data seamlessly. The text mining software can analyze qualitative words to determine their relationship with other variables provided in a claims report. It can then determine whether the claim is genuine or not. Additionally, enterprises can search for information and access them quickly, with text mining. Thus, enterprises can quickly process customer claims while also keeping a check on fraudulent ones, ensuring they don’t face unnecessary financial losses.
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5. Improving data management and retrieval
As mentioned earlier, enterprises face difficulty in managing and retrieving information from unstructured data. Enterprises usually gather data from multiple sources. Managing it in a single, secure location is difficult. With text mining, data can be managed in a reliable manner.
Enterprises can manage data in a single secure database with data management software based on text mining. Similarly, only the data relevant to the search query can be retrieved with the help of text mining tools. The process of filtering the required information in a short period of time is made possible with text mining tools.
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6. Risk Management
Analyzing, identifying, treating, and keeping track of the risks present in a given action or process inside an organization is known as risk management. Inadequate risk analysis is frequently a major factor in disappointment. It enables timely access to the required records.
7. Social Media Analysis
There are various text mining tools available that are specifically made for examining how social media networks are implemented. This enables the tracking and clarification of texts produced online by news, blogs, emails, etc.
8. Business Intelligence
Text mining techniques are now being used by businesses and business enterprises as a key component of their business information. Text mining techniques help organizations assess the strengths and weaknesses of their competitors, giving them a competitive edge in the market in addition to providing important insights about user behavior and trends.
Text Mining Techniques
Given below are the most famous text mining techniques.
1. Information Extraction
Information extraction is the most famous technique used in text mining. The process of separating pertinent information from enormous quantities of textual material is referred to as information exchange. Identifying the extraction of entities, properties, and their relationships from unstructured or semi-structured texts is the main goal of this text-mining technique.
2. Information Retrieval
The practice of collecting pertinent and related patterns from a particular set of words or phrases is known as information retrieval (IR). IR systems use several algorithms in this text-mining technology to follow and monitor user behaviors and find pertinent data as a result. The two most well-known IR systems are Google and Yahoo. Information retrieval is the method employed in text mining that is most well-known.
One of the “supervised” learning methods used in text mining, this method categorizes natural language documents according to their content and assigns them to one of a predefined set of subjects. In order to find the appropriate subjects or indexes for each text document, categorization, or more precisely Natural Language Processing (NLP), is a process of gathering text documents and processing and analyzing them. In NLP, the co-referencing technique is frequently used to extract pertinent synonyms and abbreviations from textual input.
One of the most important text-mining approaches is clustering. It looks for inherent textual informational structures and classifies them into useful subgroups or ‘clusters’ for additional examination. Standard text mining tools like cluster analysis help distribute data or serve as a pre-processing step for other text mining algorithms that run on identified clusters.
The practice of automatically creating a compressed version of a certain text that contains useful information for the end-user is known as text summarization. This text mining technique aims to browse through numerous text sources to provide succinct summaries of texts that contain a significant amount of information while essentially maintaining the overall content and intent of the original documents.
Text mining applications can be found in all the major sectors, right from insurance to customer services to digital marketing. And these are only a handful of the limitless text mining applications that we have talked about in this piece. With proper knowledge and understanding of text mining tools and techniques, text mining applications can be used in any process that involves textual data.
We hope that this piece helped you understand various text mining applications across various industries. To learn more about text mining and pursue a career as a data scientist in any of the above-mentioned sectors, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
What is the difference between text mining and data mining?
Data mining is a statistical method in which the raw data is processed to extract meaningful information for the benefit of the company. To gather the information, pre-existing documents and sheets are used. Statistical techniques are used to process the raw data. Text mining is a sub-domain of data mining where the text is processed from the given documents to gather meaningful information. Instead of documents, the text is used to extract the information. The data is linguistically processed and hence computational linguistic methods are used in text processing.
What is unstructured data and what are its examples?
The data that is not arranged according to any pre-set data model is known as unstructured data. Out of all the data generated, around 80-90% of data is unstructured and its rate of generation is much faster than the structured data. The unstructured data can not be stored in relational databases or RDBMS. Since it comes in multiple formats, it is very difficult for traditional software to process this data. Below are some of the most common examples of unstructured data. Email message fields are unstructured but email metadata is structured to some extent and hence email is often considered semi-structured data. Text files like spreadsheets, word documents, presentations, and log files are all unstructured.
How can you detect frauds with text mining?
It happens often that people make false insurance claims and hence it is highly necessary to detect these frauds so that innocent people do not have to face the consequences because of these frauds. Now, since the whole insurance claim is dependent on unstructured data, it becomes very difficult for the companies to process and analyze such a large volume of data. With text mining applications, enterprises can manage and analyze customer data seamlessly. You can determine some selective words which will act as the filter to detect frauds