Why is NLP Important? Top 8 Applications of NLP in 2022

Natural language processing (NLP) is an artificial intelligence domain that aids computers in comprehending, interpreting, and manipulating human language. In order to bridge the gap between human communication and machine understanding, NLP draws on a variety of fields, including computer programming and computational linguistics. Read on to learn why is NLP important and what are its applications.

Why is NLP important?

Natural language processing enables computers to converse with humans in their native tongue and scales other language-related activities. This technology enables computers to read information, hear voices, evaluate them, determine sentiment, and determine which bits are important. Machines can now interpret more language-based data than people can, without becoming fatigued and in a uniform and unbiased manner. Given the massive volume of unstructured data generated daily, from health records to social networks, automation will be essential for efficiently analyzing text and audio data.

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The complexity and diversity of human language are remarkable. We can communicate in a variety of ways, both verbally and in writing. There are many languages and dialects, and each one has its own set of grammar and syntax rules, as well as words and slang. We frequently misspell, truncate, or omit punctuation when writing. We have regional accents, mumble, stutter, and borrow terminology from other languages when we talk.

While unsupervised and supervised learning, as well as deep learning, are now commonly employed to mimic human language, there is still a requirement for syntactic and semantic knowledge, as well as domain expertise, which current ML approaches do not always provide. NLP is significant because it helps resolve linguistic ambiguity and gives helpful quantitative structure to data for many application areas, such as voice recognition and text analytics.

What are the applications of NLP?

Now, let’s take a look at some of the applications of NLP in various sectors.

Market Intelligence

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Marketers can utilize natural language processing to gain a deeper understanding of their clients and use those insights to develop more effective tactics. They can analyze subjects, keywords, and make effective use of unstructured data thanks to the power of NLP. It can also be utilized to determine customer pain areas and monitor your competition.

Sentiment Analysis

During discussions, humans have the ability to be sarcastic and sardonic. With real-time sentiment analysis, you can keep an eye on social networking mentions and intervene before things get out of hand. This NLP application allows your firm to sense the pulse of its customers. It also allows you to evaluate how your clients reacted to your most recent digital marketing campaign. Companies can conduct sentiment analysis on a regular basis to gain a better understanding of their business.

Hiring and Recruitment

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We can all concur that one of the most critical responsibilities of the HR department is to hire the right people. However, in today's environment, HR has so much data that sifting resumes and shortlisting individuals have become daunting.

This work can be made easier with the help of NLP. HR experts can use techniques like extracting features and named entity detection to extract information from candidates such as their names, talents, locations, and educational histories. This enables unbiased resume screening and the selection of the best candidate for the job.

Text Summarization

This NLP application is used to extract the most important information from a text and summarize it. The main goal is to make sorting through huge amounts of data in news items, official papers, and scientific studies faster. Natural language processing may be used for text summarization in two ways: extraction-based summarization, which takes essential words and provides a summary without contributing any further information, and abstraction-based summarization, which restates the original content to generate new terms.

Survey Analysis

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Surveys are an important tool for companies to evaluate their performance. Whether it's gathering feedback on a new product launch or determining how well a company's customer service is performing, survey analysis plays a critical role in identifying flaws and assisting companies in improving their goods.

The issue emerges when a huge number of clients complete these surveys, resulting in extremely big data sets. The human brain is unable to comprehend everything. That is why the NLP technology was introduced. These methods help organizations get accurate information about their consumers' opinions and improve their performance.

Targeted Advertising

Businesses continue to place a premium on lead generating. This is the primary reason they want to connect to as many people as possible. Natural Language Processing (NLP) is a fantastic tool for getting the appropriate ad in the right place at the right time. This is accomplished through keyword research, user browsing trends on the internet, emails, and social media platforms. Text mining software is used to carry out these activities.

Neural Machine Translation

This is one of the earliest NLP applications, and it is still important in 2021. A neural network is used by a machine translation to interpret low-impact content and speed up a conversation with its users. An encoder, a bidirectional recurrent neural network, converts a source sentence into matrices for a decoder, another recurrent neural network. As we can see in Google Translate, this aids in the prediction of terms in the target language.

Copywriting

Thanks to NLP, a work that requires creativity and is completed while keeping the company's vision in mind is now achievable. It helps businesses flourish by improving their content marketing methods. This application of NLP in the business world uses the technology to generate marketing content that is consistent with the brand voice while also providing information into which messages resonated with the target audience.

Conclusion

Businesses demand data, yet the vast majority of that data is unstructured and inaccessible (over 80%). In this scenario, Natural Language Processing (NLP) has proven to be a lifeline.

NLP allows businesses to provide apps to customers who require reliable data analysis. This capability allows for efficient human-computer interaction as well as the analysis and structuring of vast amounts of previously unprocessed data.