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5 real-world applications of natural language processing (NLP)

5 real-world applications of natural language processing (NLP)

Natural language processing (NLP) is a field of study that focuses on enabling computers to understand and interpret human language. NLP involves applying machine learning algorithms to analyze and process natural language data, such as text or speech.

NLP has recently been incorporated into a number of practical applications, including sentiment analysis, chatbots and speech recognition. NLP is being used by businesses in a wide range of sectors to automate customer care systems, increase marketing initiatives and improve product offers.

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Specifically, this article looks at sentiment analysis, chatbots, machine translation, text summarization and speech recognition as five instances of NLP in use in the real world. These applications have the potential to revolutionize the way one communicates with technology, making it more natural, intuitive and user-friendly.

Sentiment analysis

NLP can be used to analyze text data to determine the sentiment of the writer toward a particular product, service or brand. This is used in applications such as social media monitoring, customer feedback analysis and market research.

A common use of NLP is sentiment analysis of the stock market, in which investors and traders examine social media sentiment on a particular stock or market. An investor, for instance, can use NLP to examine tweets or news stories about a specific stock to ascertain the general attitude of the market toward that stock. Investors can determine whether these sources are expressing positive or negative opinions about the stock by studying the terminology used in these sources.

By supplying information on market sentiment and enabling investors to modify their strategies as necessary, sentiment research can assist investors in making more educated investment decisions. For instance, if a stock is receiving a lot of positive sentiment, an investor may consider buying more shares, while negative sentiment may prompt them to sell or hold off on buying.


NLP can be used to build conversational interfaces for chatbots that can understand and respond to natural language queries. This is used in customer support systems, virtual assistants and other applications where human-like interaction is required.

A chatbot like ChatGPT that can help consumers with their account questions, transaction histories and other financial questions might be created by a financial institution using NLP. Customers can easily obtain the information they require thanks to the chatbot’s ability to comprehend and respond to natural language questions.

Machine translation

NLP can be used to translate text from one language to another. This is used in applications such as Google Translate, Skype Translator and other language translation services.

Similarly, a multinational corporation may use NLP to translate product descriptions and marketing materials from their original language to the languages of their target markets. This allows them to communicate more effectively with customers in different regions.

Text summarization

NLP can be used to summarize long documents and articles into shorter, concise versions. This is used in applications such as news aggregation services, research paper summaries and other content curation services.

NLP can be used by a news aggregator to condense lengthy news stories into shorter, easier-to-read versions. Without having to read the entire article, readers can immediately receive a summary of the news thanks to text summarization.

Related: 7 artificial intelligence (AI) examples in everyday life

Speech recognition

NLP can be used to convert spoken language into text, allowing for voice-based interfaces and dictation. This is used in applications such as virtual assistants, speech-to-text transcription services and other voice-based applications.

A virtual assistant, such as Alexa from Amazon or Assistant from Google, uses NLP to comprehend spoken instructions and answer questions in natural language. Instead of having to type out commands or inquiries, users may now converse with the assistant by speaking.