How Does Data Analytics Use Natural Language Processing?


The fascinating field of natural language processing, or NLP, contains the secret to understanding your continuously expanding unstructured data collection. Data that is vital for streamlining operations and enhancing your company is provided to you by everyone from customers to staff, but it is kept locked behind a format that computers cannot understand.

NLP drives NLP analytics solutions and is the translator between humans and computers. Once the computer understands how to work with text data, it can process the data and reveal various insights.

How is NLP usually Implemented?

Although some laws govern human speech, there are several exceptions to this pattern that call for using AI-powered NLP. Consider how challenging it would be to incorporate all of the rule exceptions, slang phrases, and other linguistic variations that develop throughout time.

NLP employs data science techniquesto develop such knowledge on its own. The technology can adjust to linguistic changes over time and tailor itself to the kind of unstructured data your business often deals with. It’s not difficult to get the solution up and running with real-world data because you undoubtedly have a sizable backlog of unstructured data that can use processing.

But What is NLP Data?

Responses to open-ended inquiries are among the most popular types of NLP data. The computer requires assistance in understanding the genuine meaning and intent of the user because this text input doesn’t fit into a predefined response. Sentiment analysis lets you enter the audience’s mind and better understand their decision-making processes because emotions can influence many decisions. Speakable language is another type of NLP data frequently used in products with speech-to-text functionality. For detailed information about NLP data and its types, refer to the data science certification course, offered by Learnbay. 

Natural Language Processing Applications in Data Analytics:

In recent years, the field of data analytics has experienced rapid developments partly because of the development of tools and technologies like machine learning and NLP. Nowadays, it is possible to have a far better understanding of the material in papers than in the past. Here are a few instances of how NLP is used in data analytics:

  • Outside of data analysts and data scientists, more people within a specific organization can now interact with data thanks to NLP. Because data can be treated conversationally while still delivering the same essential insights about the data, this communication is more natural for non-technical team members.

  • Surveys can provide insightful data on a company’s success. However, when many clients answer surveys, the volume of data likewise increases. A single person can no longer read the results and draw a conclusion. Employing NLP enables businesses to manage survey results and gain insights much more accurately and quickly than they could by hand.

  • Compared to humans, machines can analyze a significantly greater volume of linguistic data without the possibility of bias, inconsistent results, or exhaustion. Text and audio data can be swiftly and comprehensively evaluated using automated data analysis skills.

  • Business intelligence and analytics systems are incorporating NLP capabilities, which can improve natural language creation for data visualization storytelling. This makes data visualizations more comprehensible and available to a wider range of users. In addition to improving the storytelling experience, narrating data visualizations reduces the possibility of subjectivity in data interpretation.

  • NLP provides a method for tracking social media discussion on a certain problem and answers to it and identifying key influencers.

  • When incorporating machine learning into a system, carefully selected training data must be used. However, unstructured text is frequently the only data type present in sources like electronic health records. Electronic health records can retrieve clean, organized data that NLP can then use to feed into complex predictive machine-learning models. This technique eliminates the need for costly manual data annotation.

  • The pace at which data may be studied is changing thanks to NLP. Now, query generation and response discovery capabilities in visualization software match the speed at which inquiries can be spoken or written.

  • It is not simple to understand human language. People express themselves in various ways, both verbally and in writing. There are nuances to each of the hundreds of languages and dialects currently in use, including grammar and syntax rules, regional accents, and slang expressions. Textual analytics and speech recognition are made easier by NLP’s ability to clear up linguistic ambiguities and provide the data with a useful quantitative structure.

  • In text mining, a branch of artificial intelligence, natural language processing (NLP) is used to transform unstructured text from documents or databases into structured data that can be evaluated or used in machine learning algorithms. Once the data is formatted, it can be added to databases, dashboards, or data warehouses, where it can be utilized for various analytical investigations, including descriptive, predictive, and prescriptive ones.

Significance of NLP analytics for businesses:

Flying blind in the face of text data will not position your company for long-term success. NLP analytics allow you to:

  • Process distinct survey responses without requiring a lot of manual labor: Employees may put in hundreds or even thousands of hours on a single project if required to go through each response manually, code it, and attempt to understand any general trends. This functionality is performed by NLP analytics in a fraction of the time and with no chance of human error.

  • Learn what customers say about your goods and services on social media: It can be tedious to sift through tens of thousands of social network comments, and crucial information might get lost there. Whether you’re handling a social media crisis or want to know what people say about your most recent announcement, NLP analytics enables you to delve deeper into the comments.

  • Investigate consumer perceptions of novel goods and services: You don’t want to wait months to determine whether customers like your most recent products. These insights are swiftly gathered using data analytics so that decisions can be made based on new responses which can be learned in the best data analytics courses, available online. 

  • Determine which of your most important clients are angry, outraged, dissatisfied, or otherwise in a situation where your customer experience isn’t up to par:

They will publicly announce to everybody in their social circle who would listen that this audience segment has one foot out the door. You may win back these clients by providing quick resolution, specialist resources, and case escalation.

  • Identify areas of process inefficiency, prospective investment areas, and other improvements using employee feedback: When you have the chance to talk to personnel at different levels of the firm, you can learn a lot about it. Although you might not have time to speak with each person one-on-one, NLP analytics can use their open-ended responses to offer you insights on a continuing basis.

Final thoughts:

NLP has been able to alter how organizations interact and evaluate language-related data in a tech-driven world. The practical method of teaching computers a genuine sense of text and speech to carry out automated jobs is, after all, revolutionary.

NLP improves effectiveness, whether it is used for categorization, extraction, summarization, or translation. The promise of speedier natural language processing skills within AI and ML domains is made possible by increased development and research in linguists, machine learning, and computer science.


Natural language processing will also become much more widely available in the near future. With data science and analytics, you can now customize modifications and automate procedures to understand customer behavior broadly. For more information, check out the data science course with placementand acquire hands-on practical knowledge of data science and NLP techniques.


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