How is Natural Language Processing Used in Data Analytics?

What is Natural Language Processing?

Natural Language Processing (NLP) is a subfield of artificial intelligence that studies the interaction between computers and languages. The goals of NLP are to find new methods of communication between humans and computers, as well as to grasp human speech as it is uttered. This technology combines machine learning with computational linguistics, statistics, and deep learning models so that computers can process human language from voice or text data and grasp its entire meaning, as well as the writer or speaker’s intentions.

NLP is often used for developing word processor applications as well as software for translation. In addition, search engines, banking apps, translation software, and chatbots rely on NLP to better understand how humans speak and write. This article will explore how NLP is used in the field of data analytics.

Uses of Natural Language Processing in Data Analytics

The field of data analytics has been rapidly evolving in the past years, in part thanks to the advancements with tools and technologies like machine learning and NLP. It’s now possible to have a much more comprehensive understanding of the information within documents than in the past.

Here are just a few of the ways NLP is used in data analytics:

• Thanks to NLP, more people within a given organization (besides data analysts and data scientists) are now able to interact with data. Because data can be approached in a conversational manner, this interaction is more natural for non-technical team members and still offers the same important insights about the data.
• Surveys can provide helpful insights into how a company is performing. However, when a large number of customers complete surveys, the data size also increases. At that point, it’s no longer possible for one person to read the results and formulate a conclusion. Companies that use NLP to manage survey results and gather insights are able to do so much more accurately and efficiently than a human would be able to.
• Machines are able to analyze a much larger amount of language-based data than a human can, without the risk of bias, inconsistency, or fatigue. By incorporating automation capabilities into data analysis, text and speech data can be quickly and thoroughly analyzed.
• NLP capabilities are being incorporated into business intelligence and analytics products, which can enhance natural language generation for data visualization narration. By doing so, data visualizations are more understandable and accessible to various audiences. The act of narrating data visualizations not only creates a more effective storytelling experience, but it also makes it less likely that the data will be interpreted subjectively.
• When applied to social media, NLP provides a way to monitor awareness and responses about a given topic, as well as to pinpoint key influencers.
• In order to apply machine learning to a system, it’s essential to use well-curated input that can be used for training purposes. However, sources like electronic health records typically only have data in the form of unstructured text. By applying NLP to electronic health records, it’s possible to gather clean and structured data that can subsequently be used for advanced predictive machine learning models. This process eliminates the need for costly manual data annotation.
• NLP is changing the speed at which data can be explored. Visualization software can now generate queries and find answers to questions as quickly as these questions can be uttered or typed.
• The ability to understand human language is no easy task. People have many manners of verbal and written expression. In addition to the hundreds of languages and dialects currently being used, there are nuances to each of these, such as grammar and syntax rules, regional accents, and slang expressions. NLP can resolve language ambiguities and provide a helpful numeric structure to the data, which aids with textual analytics and speech recognition.
• NLP has applications for investigative discovery. It is a powerful tool for spotting patterns in written reports or emails, which can be used not just to detect but also to solve crimes.
• Text mining is a type of AI that incorporates NLP to convert unstructured text within documents or databases into structured data that can then be analyzed or used for machine learning algorithms. Once the data is structured, it can also be incorporated into data warehouses, databases, or dashboards, at which time it can be used for various types of analytical analyses, such as predictive, prescriptive, and descriptive.
• By incorporating keyword extraction algorithms to reduce a large body of text into several ideas and keywords, it’s possible to glean the main topic of the text without having to read the document.
• When working with a text, text statistics visualizations can offer valuable insights about sentence length, word frequency, and worth length, and display this information in histograms or bar charts.
• Named Entity Recognition, or NER, can be used to retrieve specific entities from a body of text, like places, dates, or names.
• Sentiment analysis is one of the primary functions of NLP. The main use of sentiment analysis is to analyze the words in a text so that the general sentiment of the text can be established. This technique is able to reduce results into three areas: positive, negative, and neutral. Results that offer a negative number indicate that the text has a negative tone; those with a corresponding positive number indicate positive sentiments in the text.

Since the advent of computers, humans have searched for ways for computers to comprehend and communicate with users using spoken language. As new technology and machine learning algorithms are introduced, it’s now possible for computers to understand and converse with humans more than ever before. NLP is a powerful way for humans and computers to interact verbally and through written text.

The field of data analytics is being transformed by natural language processing capabilities. In the coming years, as technology continues to change and inform how humans interact with computers, as well as how computers handle big data, the field of data analytics is expected to continue to evolve in new and exciting ways.

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