Text Mining Examples


As the amount and relevance of unstructured data increases, so text mining is a necessity. Applications like Risk Management Software have improved profits thanks to text mining’s ability to advance our understanding about risks associated with cybercrime or financial issues.

Text mining is a technique that can extract useful information and knowledge from unstructured text, such as business deals or customer reviews. Unstructured data is simply content that is not yet neatly placed into a spreadsheet format. Text mining is being used in large organizations to discover insights about trends within their data sets.

Text Mining is an innovative technology that can help organizations. The following ten text mining examples give you a glimpse into its potential for the future:

Risk Management

With the rise in concern over cyber-attacks, it is more important than ever to protect against risk. One way that this can be done successfully is by using text mining technology which identifies potential security threats. It analyzes vast quantities of documents and data streams that are stored electronically or on paper together with information sources such as images or sounds.

Knowledge Management

Rather than trying to find information in a sea of documents, knowledge managers use software that searches texts for keywords and phrases. This allows them to quickly sift through large volumes of data with the goal being that of product development.

Cybercrime Prevention

The anonymity of the Internet and many of the communication channels allows opportunities for cybercrime. Text mining coupled with anti-crime applications are making an impact on Internet-based crimes.

Customer Care Service

Many customers are now using text analytics software to improve their experiences with the help of various sources. These may include surveys, trouble tickets, and customer call notes. These sources are valuable information that can be used by companies for better quality service delivery and faster resolutions.

Fraud Detection Through Claims Investigation

Text analytics is a tremendously effective technology in any domain where most of the information may be found as text. Insurance companies are taking advantage of it by using this kind of research. It combines results from mining through texts with other structured data to prevent frauds and efficiently process claims.

Contextual Advertising

Digital advertising is a new and growing field for text analytics. Unlike cookie-based approaches, contextual advertising provides better accuracy as well as preserves the user’s privacy.

Business Intelligence

The decision-making process is a delicate balance between strategy and analytics. The large companies use text mining for this because it helps them make quick decisions on data volumes that are too broad or complicated to analyze traditionally.

Content Enrichment

Text analytics is the process of discovering information from texts. It can be used to enrich content, provide tags for organizing, and summarizing available information in a scalable way that makes it suitable for many purposes.

Spam Filtering

Email is an effective, fast, and affordable way to communicate with your customers. But it does come with its downside: spam. Today’s issue of email service has led Internet providers increasing their costs for managing these emails as well as updating hardware and software. Text mining techniques can be implemented towards improving statistical-based filtration methods.

Social Media Data Analysis

Social media has been one of the most important sources for businesses to gain intelligence on how their customers feel about them. Companies are using prolific social media data to better understand what people want and need from a brand or product they may be considering to purchase. This includes things such as text analytics, which can analyze large volumes of unstructured texts to extract sentiments, opinions, and emotions regarding brand products.

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