Want to learn more about NLP, and its many uses? In this video, Riccardo shares 20 natural language processing examples across a wide range of industries. If you are a subscriber of this channel, the term Natural Language Processing (also known as NLP), won’t be new to you, however…
If this is the first time you’re hearing about NLP , you want to know that it basically deals with using computers to derive meaning from human languages. This might seem like a pretty innovative and cutting edge technology, but the truth is that NLP is something that’s been part of our lives for years now. In fact, consumers from across the globe interact with NLP on a daily basis, without even realizing it.
Natural Language Processing (NLP) examples for Business:
1. Social media monitoring: Tools like Hootsuite or Buffer help you to monitor social media channels for mentions of your brand, and alert you when consumers are talking about your brand.
2. Sentiment analysis: Identifying opinions and determining whether the author of the post holds a positive, negative, or neutral opinion towards a brand.
3. Text analysis: By analysing text and extracting different types of key elements (such as topics, people, dates, locations, companies), companies can better organize their data, and from there, identify useful patterns and insights. For instance, eCommerce or manufacturers can conduct text analysis of their product reviews in order to find out what customers like or dislike about their products, and how customers are using their products. To do this, these companies need NLP-powered tools like the one developed by my company Wonderflow.
4. Survey analytics: Automate the process of surveying your entire database of 10,000 customers using an NLP-equipped tool.
5. Spam Filters: Among other factors (deliverability, email domains, etc), these filters use NLP technology to analyse email subject lines and their body content.
6. Email classification: Here, Gmail uses NLP to identify and evaluate the content within each email, so that it can categorize them accurately in three tabs — Primary, Social, and Promotions. The system isn’t 100% foolproof, though, which is why you might find some newsletters (especially ones that contain more text than images) getting filtered to your Primary tab.
7. Autocomplete: To suggest relevant keywords for you, Google relies on a treasure trove of data that catalogs what other consumers are looking to find when entering specific search terms. To make sense of that data and understand the subtleties between different search terms, the company uses NLP.
8. Autocorrect: NLP identifies the closest possible term to your misspelling and automatically changes your misspelled term to the accurate one instead.
9. Spell check: Spell check aside, other writing tools such as Grammarly, ProWritingAid, and WhiteSmoke also utilize NLP to correct users’ spelling and grammatical mistakes.
10. Smart search: With NLP, a store can pick up on context and add contextually relevant synonyms to search results. This helps the store accurately predict exactly what their customers are searching for, and highlight the relevant listings.
11. Duplicate detection: Quora uses NLP to reduce the instances of duplicate questions, as much as possible. Once a user finishes typing their question, Quora then analyses it to determine if it’s linguistically similar to the other questions on its site, and serves up a list of similar questions that users can just refer to (instead of posting a new question).
12. Translation tools: Google Translate relies on NLP to understand the phrases or terms that its users are trying to translate, and the same goes for all the other alternative translation apps out there.
13. Chatbots: Using NLP and machine learning, chatbots can decipher consumers’ questions, recommend them products, book meetings and appointments for them, and more.
14. Upgraded chatbots: Chatbots can give users a high-level overview of their spending habits, highlight the special benefits and promotions that they’re entitled to, and more. As with regular chatbots, these upgraded bots also utilize NLP technology to understand users’ queries.
15. Bots x Knowledge bases: By building knowledge bases, companies are empowering their customers to resolve their own problems 24/7, instead of contacting a company’s support department, and having to wait to hear back from them. Because knowledge bases often contain thousands of documents, it’s in the company’s best interest to help their customers identify the right materials quickly. To do this, companies can link their chatbots up with their knowledge base, and configure their bots to send customers links to help docs relevant to their queries.
16. Smart home devices: Your smart home device uses NLP to recognize your voice commands and carry out the right action.
17. Automatic insights:
Imagine a tool that doesn’t just analyze or identify trends from text data, but goes one step further, and formulates insights about a product or service for a user to read. Yep, this technology actually exists… and you can use it by signing up for Wonderflow’s Wonderboard. How does this work? In a nutshell, Wonderboard draws on the text data that you’ve fed it to compose sentences by simulating human speech. Again, it uses NLG, natural language generation, and machine learning to do this.
18 Algorithmic Trading: NLP is crucial in helping programs make sense of data and information and come up with recommendations on what stocks an investor should buy or sell.
19. Streamlining patient information: Clinics and medical companies are now using NLP to streamline patient information, and automate the process of understanding a patient’s condition.
20. Aircraft maintenance: By utilizing NLP tools, mechanics can more easily synthesize information from immensely wordy aircraft manuals. On top of that, aircraft companies can also use NLP to analyze its reports submitted by pilots or other aircraft personnel, and improve their processes and systems from there.
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