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NLP Business Examples

NLP enabled application make a good fit for businesses where analysing or processing text is done at large. Below some example NLP business use cases.

  • Social media monitoring:Almost all social media monitoring tools are basically built using NLP technology. These tools help to monitor social media channels for mentions of your brand, and alert you when consumers are talking about your brand. Real time monitoring of social media channels is important for companies for a lot of large companies, e.g. to ensure that any potential crises is noticed immediately.
  • Sentiment analysis: Sentiment analysis is a smaller subset of social media monitoring. It refers to monitoring the social media landscape and listening in on conversations and identifying opinions and determining whether the author of the post holds a positive, negative, or neutral opinion towards a brand. Using NLP sentiment analysis tools is it easy to filter emotionally-charged words that are used to describe a brand or a customer's experience with a brand. It is also possible to automate research on how consumers speak about a product or service.
  • Text analysis: Text analysis can be broken into several sub-categories e.g. grammatical, syntactic and semantic analyses. By analysing text and extracting different types of key elements (such as topics, people, dates, locations, companies), it is easier to organize data and identify useful patterns and insights.
  • Survey analytics: If you are analysing large amounts of surveys you can use old-skool reporting and statistical techniques. But a NLP based analyse makes it possible to faster and easier find patterns, categories and anomalies that are hard to find manual in survey responses with more than 20.000 responses.
  • Spam filters: Emails that contain text and words such as "free", "promotion", "buy now" , 'coin' , 'offer' , etc, have a high chance of being spam. With the use of NLP technology you can create self learning spam filters. Sometimes too good.
  • Autocomplete: With an autocomplete function it is possible to predicts what the next characters or words will be that you will enter. This makes the use of text based UI simpler.
  • Hiring Tools: Large companies still do receive many resumes, analysing can be time-consuming and the task of sorting overwhelming. Natural language processing software is able to speed up the process of sorting resumes. But be aware NLP software is not flawless. So you will miss good candidates if you only scan resumes.
  • Conversational Search: Traditional search often brings not the results you want. Wit conversational search the context of your search is taken into account. So only for the current context relevant search items will be presented. Since searching quality information is time consuming , streamlining search in real-time conversation NLP software will improve productivity for knowledge workers.