Natural Language Processing (NLP) is a revolutionary branch of Artificial Intelligence that enables machines to recognise, understand and process human language and make critical decisions based on the data. It is a combination of computational linguists (application of computers to analyse and comprehend written and spoken language) and statistical and machine learning models. These technologies merge with each other to extract human language in the form of text or voice data and decipher the meaning, intent and sentiment of the user. The technology is rapidly advancing because of enhanced human-to-computer communication, availability of voluminous data, powerful computing and strong algorithms.
Importance of NLP
Businesses, on a daily basis, encounter and use staggering amounts of data that are unstructured, complex and text-heavy. NLP methods are largely used by businesses to learn about the users through translation software, voice assistants, spam filters, chatbots, search engines, grammar correction software and social media monitoring tools.
NLP makes it possible for computers to read, interpret and measure sentiments of written and spoken words. Sentiments across several online posts or call logs and comments and reactions across digital platforms are recognised and deciphered. Different aspects like syntax, semantics, pragmatics, and morphology are accurately analysed. Then, computer science transforms this linguistic knowledge into rule-based, machine learning algorithms that can solve specific problems and perform desired tasks.
Applications of NLP in business
Natural Language Processing technology is extremely valuable for businesses. It plays a crucial role in streamlining business operations, enhance employee productivity and simplify mission-critical business processes. From e-commerce, healthcare, finance and advertising, NLP is used in a variety of business contexts. Let’s look at the modern-day business applications of NLP:
Sentiment analysis
Sentiment analysis is understanding an opinion, feedback and emotions through written or spoken language. It interprets and classifies emotions in data or understands the emotional context behind words. This is used to measure customers opinions and feedback on a product or service. It helps the business understand how their product is doing based on positive, negative and neutral emotions expressed across various platforms — social media, emails or survey reports. If social media posts about a newly-launched cosmetic product have words like ‘fantastic’, ‘amazing’, the overall sentiment is gauged as positive by the NLP tool.
Text analytics
Businesses gain insights from words and phrases used by customers or clients across various platforms — emails, chats, social media, news, surveys, etc. Text analytics can be broken down into various subcategories such as grammatical, morphological, syntactic and semantic analysis whereby words in the documents and resources can be analysed for meaning and intent of users, thereby helping businesses solve problems and delivering actionable outcomes. NLP also helps in text analytics through keyword extraction and finding structure or patterns from a bunch of unstructured paragraphs from different sources. For example, an e-commerce platform can use text analytics to assess the product reviews that they get in plenty in their portal and use this information to change or better their products.
Language translation
With businesses scaling their operations across borders and communication transcending geographical boundaries, the need for language translation has become significant. NLP technology provides speech to text translations, quickly and efficiently. With NLP, online translating tools can understand different orders of sentence structure, syntax and phonology and translate languages more accurately and give out grammatically correct results.
Chatbots and virtual assistants
These are programmes that simulate and understand human conversations. Chatbots and virtual assistants use NLP to identify relevant topics, understand the intend behind a sentence and decipher questions and comments, and come up with the best recommendations, solutions and responses based on the interpretation of the data. These intelligent machines act as customer support across businesses by improving and innovating the responses and solutions based on repeated interactions with humans and thereby help solve a majority of queries of people.
Speech recognition and smart assistants
NLP transforms spoken words into a machine-readable format, enabling devices to respond to spoken commands. These devices can recognise words and speech patterns based on the voice recognition algorithm enabled by NLP. Virtual assistants like Alexa and Siri or any other smart devices use NLP to process voice requests. In businesses, speech recognition can boost productivity enormously as spoken words are converted into text documents within seconds and calls and recordings are transcribed easily, besides many tasks that can be accelerated by simply sending spoken commands to devices.
Predictive text
This is another application of NLP which makes searching, emailing and writing a document easier by letting the computer or phone finish the sentence. Autocomplete, autocorrect and predictive text identifies misspellings, grammar mistakes and provides suggestions to complete or replace a sentence. Here, NLP understands the cluster of words as a whole as well as the semantic meanings of the sentences which helps in completing the sentence.
Learn how Textrics can provide an all-in-one analysis for different formats of unstructured data. Sign up for our free demo now:
