Top 10 Applications of Text Analytics in 2023

Text Analytics: What Is It?

 

The technique of examining and drawing insightful conclusions from unstructured text data is known as text analytics. This may involve methods like topic modeling, sentiment analysis, and natural language processing (NLP), among others. Text analytics seeks to extract pertinent information from massive amounts of unstructured text data and convert it into structured data that is simple to understand and analyze.

 

Why do we need Text Analytics?

 

Several factors call for the use of text analytics:

 

1. Unstructured data: 

 

The majority of information available today is unstructured, including emails, posts on social media, and customer reviews. Text analytics gives you the tools to take this unstructured data and turn it into structured data that is simple to understand and analyze.

 

2. Business insights: 

 

Text analytics may assist businesses in learning important information about the attitudes, preferences, and feedback of their customers. This information can be used to enhance goods and services and boost client satisfaction.

 

3. Decision making:  

 

Text analytics can be used to extract pertinent facts from vast amounts of text data, and this data can then be used to support decisions in a variety of fields, including finance, healthcare, and law.

 

4. Automation: 

 

Manual analysis of the growing amount of text data is becoming impractical. Insight extraction from text data can be automated with the use of text analytics, saving time and money compared to manual analysis.

 

5. Language understanding: 

 

Understanding human-written natural language is made possible by text analytics, which can be very helpful in a variety of industries, including customer service, human resources, and many more.

 

Overall, text analytics is required to make sense of the enormous amounts of generated unstructured text data and to extract insightful information that can be utilized to enhance corporate operations, support decision-making, and give a better knowledge of the language used by humans.

 

Also Read | Introduction to Text Analytics and Models in Natural Language Processing

 

Real-world applications of Text mining:

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Applications of Text Mining

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• Reputation Management: 

 

The public perception of a business must be flawless, especially in today's cancel culture. By analyzing tweets, comments, news stories, and other feedback that mention it or anything or anybody associated with it, text mining enables you to interpret data acquired via social media listening and voice of the consumer (VoC) initiatives. 

 

Executives of the corporation, investors, employees, partners, political parties, and organizations the business supports are included in this. By taking action to avert the catastrophe, businesses can improve the state of their image in real time.

 

• Search engine optimization: 

 

The search engine may recognize spelling variations, context, and intent and label an email as spam; alternatively, it can penalize a business website that has been using keyword stuffing or other techniques to improve its search ranking. Similar to this, a text analytics API can be used to enhance and power a company's search engine. 

 

This gives you the power to semantically search all of your documents across the whole enterprise, including webinar videos, training materials, and interview transcripts. By recognizing pertinent subjects, themes, and subject matters, for instance, Repustate's search inside video feature enables you to search for words, text overlays, logos, and images throughout your complete video library.

 

• Finding patterns in data: 

 

Finding patterns in data, both historical and current, is a very significant feature in medical treatments and clinical trials, new product creation, real estate planning, and other similar highly monetized and time-sensitive industries. 

 

Businesses can use text analytics to analyze data trends for a variety of purposes, including customer behavior, events that may have an impact on oil prices and foreign exchange rates, weather predictions, and other things. Patterns and trends can also play a key role in developing new security and surveillance rules, traffic laws to ease congestion on high-frequency routes, and immigration regulations.

 

• Crime prevention and prediction: 

 

Prevention is preferable to treatment. What if you could predict where and when crimes could occur and thereby prevent them from happening? A scene from a science fiction film like Minority Report seems to be the case. Well, it is now a fact. Most criminals plot and communicate through these channels because the internet and most communication software that uses it are anonymous. 

 

You can understand that millions of everyday people also utilize these platforms for communication, making it challenging to identify messages that can be construed as threats. Advanced text analysis software makes this simple by scanning communication sources in real-time and sounding various levels of threat warning when different sorts of content are found. These technologies have been used by law enforcement agencies all around the world to thwart terrorist attacks, find sleeper cells, and deter criminal activity.

 

• Sales & Marketing: 

 

A sales team's worst nightmare is prospecting. The goal of sales teams is to increase performance and sales. A MonkeyLearn survey found that 27% of sales agents spend more than an hour each day on data entry labor rather than making deals, indicating that valuable time is squandered on administrative tasks.

 

By automating this tedious effort, text analytics tools assist to fill the marketing funnel with important and targeted insights. Just to give you a hint: To respond to client inquiries immediately, chatbots are used. The sales staff can do target marketing and advertising, make product improvements, and anticipate the possibility that a customer would purchase a product by analyzing this data.

 

• Consumer Assistance: 

 

Continuous efforts are made by businesses to provide seamless customer service. Customer service shortcomings account for a large portion of the customer turnover factors. You can compile customer complaints, questions, and comments using text analytics tools to automate customer service procedures. 

 

This can assist in automatically routing tickets to eliminate human labor and errors in addition to enhancing responsiveness. As an illustration, the algorithm extracts the phrase "My purchase isn't delivered yet" from customer inquiries, and then uses text analytics techniques to compare and match this phrase with the tag "Delivery Issues" Text analytics technologies will also aid in creating individualized customer services, hiring the best candidate, and effectively setting priorities.

 

• Information Management: 

 

Managing a large amount of textual data has grown difficult in many businesses, including the healthcare sector. The amount of data gathered each hour is enormous. All of this information must be kept in a way that makes it retrievable whenever needed. In the event of an epidemic, hospitals may work together to analyze all of their data to identify the initial infection or the source. 

 

Without the aid of adequate text analytics solutions, managing the data and information and maintaining them in an organized tree-like fashion, such a massive exercise would be impossible. People would then be able to access the data in whatever way they require—based on geography, gender, disease, and more. Such businesses working with vast numbers of text documents may become crippled by the inability to locate crucial information promptly.

 

• Detection of Fraud by Insurance Companies:

 

In light of the rise in insurance fraud cases, text analytics has been successful in analyzing enormous databases of case files to determine the likelihood that an insurance claim is fraudulent. Since the fraud recognition software would automatically indicate cases where a high risk of fraud was established, it significantly reduces the labor of the company officials. 

 

The program serves as a filter so that human attention is only focused on situations that require it, even though it is not completely foolproof. To fully benefit from text mining technological advancements and combine their findings to create structured data, insurance companies are forming partnerships with industry titans to process claims quickly and stop fraud.

 

• Filtering spam: 

 

In most organizations, emails are still seen as the most official form of communication. Spam is its negative side, which has gotten worse in the twenty-first century. At least nine of every ten emails in my inbox are spam. Spams not only take up space but also act as a gateway for scams, infections, and other threats. 

 

To filter out more spam emails and provide the user with a better experience, businesses are working hard to filter out more and more spam using intelligent text analytics as opposed to the keyword matching utilized earlier. If you own a company that can benefit from text mining and text analytics, keep in mind that knowledge is power. Before choosing how to use data to your advantage, make sure you speak with someone who has done so themselves or who has assisted others in doing so.

 

• Social media listening: 

 

When a business pays close attention to its clients, it may accomplish a lot. Customer time is required for surveys, although comments on social media are typically spontaneous and informal. As a result, there is a lot more information about topics that a poll wouldn't have covered that may be learned from social media listening. 

 

Additionally, social media has made it feasible for businesses to communicate directly with their clients, resulting in more individualized connections and occasionally even humorous online exchanges. Several businesses, including Netflix, McDonald's, Wendy's, and even Microsoft, have engaged in amusing and witty social media conversations with their customers while also using this information to enhance their user interface and customer service.

 

Also Read | 6 Text Mining Techniques for Brand Image Analysis

 

Procedures for text mining

 

Depending on the particular application, text mining can include different procedures, but often involves the following:

 

• Data collection: It is the process of acquiring text data from numerous sources, including emails, social media, and consumer evaluations.

 

• Data pre-processing: The cleaning and preparation of text data for analysis, which may involve operations like tokenization, stemming, and lemmatization as well as the elimination of noise and unimportant data.

 

• Exploratory data analysis: It is the process of examining text data to find patterns, trends, and relationships that can be utilized to better comprehend the data.

 

• Feature extraction: Word frequency and emotion are two examples of significant qualities that can be extracted from text data and used in subsequent analyses.

 

• Modeling: Creating models for named entity recognition, subject modeling, and sentiment analysis to examine text data.

 

• Evaluation: Analyzing the models' performance, including their accuracy, precision, and recall, and making adjustments as needed.

 

• Insights and visualization: Taking important insights from the text data and putting them into a clear, comprehensible visual form.

 

• Deployment: Setting up the models in a setting where they can be utilized to instantly evaluate new text data.

 

These are the most typical processes that are involved in text mining, albeit they may slightly change depending on the issue statement and the demand.

 

Also Read | Feature Engineering: Process and Techniques

 

Final Note

 

Text analytics adds speed and accuracy for a quantitative, rather than a qualitative, assessment of text data, which has been a huge aid in extending and enhancing human abilities. This will only get worse when AI is employed more and more. 

 

Beyond chatbots, text analytics is the foundation of any artificial intelligence (AI) system that depends on verbal communication between humans. It can also be used to extend and learn from analysts' evaluations of textual data in a variety of different fields as a key component of AI systems for product development.