Today I will talk about Natural language processing NLP and I will share 20 examples to make sure we all understand what it really is. Hello, everyone! I am Riccardo Osti. And on a daily basis, I help the most successful companies to increase their profitability by investing in the consumer experience. If you are a subscriber of this channel, the term NLP won’’t be new to you. However, if this is the first time you’re hearing about Natural Language Processing (also known as NLP), you want to know that it basically is using computers to derive meaning from human languages. Now 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. In fact, customers from across the globe interact with NLP on a daily basis without even realizing it Want to learn more about NLP and its many uses. In this video, I share 20 natural language processing examples across a wide range of industries. So What Is Natural Language Processing To expand on our earlier definition? NLP is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. To learn more about NLP. You watch my other video here. Now that You’’ve got a better understanding of NLP let’s deep dive into these 20 natural language processing examples that showcase. How versatile. NLP is 1. Social media monitoring Top on our list of natural language processing examples is none other than… social media monitoring. If You’’ve ever used a social media monitoring tool such as Hootsuite or Buffer, these are basically built using NLP technology. These tools help you to monitor social media channels for mentions of your brand and alert you when consumers are talking about your brand. As many marketers and business owners will know having a negative review, go viral on social media can destroy a brand’’s reputation overnight. Bearing this in mind, it’’s important for companies to engage in social media monitoring or listening and make sure that they address any potential crises immediately 2. Sentiment analysis. Next on our list of natural language processing, examples is sentiment analysis, which is often used in social media monitoring analysis. While the latter refers to monitoring the social media landscape and listening in on conversations as a whole, the former deals specifically with identifying opinions and determining whether the author of the Post holds a positive negative or neutral opinion towards a brand Again, NLP comes into the picture here, Basically using NLP sentiment analysis tools pick out emotionally-charged words that are used to describe a brand and/or a customer’’s experience with a brand. For instance, if a post contains plenty of positive language such as “amazing” “fantastic” “wonderful”, then the tool might conclude that the overall sentiment is positive. With sentiment, analysis, companies can gauge how receptive their customers are to a particular product or service or even to a recent change that they’ve implemented (for example, a change in their returns policy support policy, etc) 3. Text analysis, Text analysis can be broken into several sub-categories, including morphological, grammatical syntactic and semantic analyses. 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. Apart from analysing their product reviews, companies can also analyse their survey results in order to come up with actionable insights Again, NLP helps these companies to make sense of all their raw data and generate useful insights and takeaways. Of course, companies who are conducting small-scale surveys might choose to manually analyse their data and come up with recommendations. That said if You’re surveying your entire database of 10,000 customers, then it isn’t feasible to sit down and sift through all the results yourself Here. Automating the process using an NLP-equipped tool makes more sense. In the end, why would you send our a net promoter score if you don’’t learn from it? If you are interested in this matter, watch this other video where I explain why you should analyse NPS data. You will also find all the links in the description. Number 5 is quite different. It’’s Spam filters. Think spam isn’’t a huge problem? Think again. According to statistics, spam accounts for 45% of all emails sent and about 14.5 billion, spam emails are sent every single day. Now, looking at those statistics, you might be wondering why you don’’t. Get more spam. Well, that’’s because we’’ve got excellent. Spam filters that flag dodgy emails as spam and prevent them from reaching our primary inboxes. How do these spam filters work? Among other factors (deliverability email domains, etc) these filters use NLP technology to analyse email, subject lines and their body content. From here, it’’s fairly easy for them to ascertain What’s spam and what’s not — emails that contain plenty of capitalized text and words such as “free”, “promotion”, “buy, now”, etc have a high chance of being spam. I hope you enjoyed the first five. If so, subscribe to my channel by hitting the subscribe button and the bell close to it. You will receive a notification when the following parts will be published. When do I post new videos? Every Monday and Thursday, So stay tuned.