MonkeyLearn was founded on the belief that developers, startups, and SMEs in every industry deserve access to customizable and affordable artificial intelligence technologies for text mining.

As a transversal internet platform, MonkeyLearn could be used in almost any internet vertical, enabling developers and startups to easily create and incorporate text mining capabilities into their own platforms, applications and websites.

The objective of these series is to expand on the different uses and applications that could be developed with MonkeyLearn. Every couple of weeks, we will share ideas and ways to take advantage of MonkeyLearn capabilities to a specific industry or vertical. For example, some of the industries we will cover include:

  • News and media:
      • Publishers can for example classify users by which content they read to:
        • Personalize the articles the user see on the homepage and improve user experience (people will see more relevant news and articles based on their interests).

          Take into consideration that we have built this demo for English Twitter users only. Having said this, with MonkeyLearn it could be very easy to do the same demo for other languages.

        • Better recommend content to your users to multiply page views.
        • Match user profiles with ads of their interests and increase advertising revenue.
      • Publishers can also classify any text content of their website or app to:
        • Better organize and index contents.
        • Automatically tag posts/comments/reviews.
  • E-commerce:
      • E-commerce sites and shops can profile users by what products or services they buy or are likely to buy too:
        • Boost sales with optimal product recommendations.
        • Improve user experience (people will see more relevant products based on their interests).
  • Advertising:
      • Ad networks and publishers can classify ads according to their descriptions to:
        • Increase ad click through rate by showing the ads in the correct context.
        • Increase ad relevance.
        • Increase ad revenue.
        • Increase campaigns ROI.
  • Social media:
      • Brands can perform sentiment analysis on their followers to know if they talk positively or negatively about their brand.
    • Companies can group tweets based on different topics to:
      • Group their users based on the topics the tweet.
      • Segment their user database.
  • Sales and customer support:
    • Sales and customer support teams can classify emails by topic to:
      • Sort emails based in the language they are written for a contact center app.
      • Send the email to the most relevant department / area / person (for example send a given email to sales, or to customer support or to the technical department, etc).
    • Sales teams can classify emails by relevance to:
      • Detect important leads and prioritize.
      • Filter those emails from poor quality leads.
  • Education:
    • Education platforms can classify profile users by their level, learning speed, which courses and educational material they are interested in to:
      • Better recommend courses, notes, papers, tutorials, videos, books and educational material to boost learning.
      • Improve user experience (people will see more relevant courses based on their interests).
    • Categorize students frequent asked questions to:
      • Better organize and index questions.
      • Given a brand new question from a student, suggest the best possible answer.
      • Improve the knowledge base.
This are just some examples of how MonkeyLearn can be used in some industries. Other industries we will cover include finance, healthcare, government, academy, marketing, business, security, hospitality and more.
Stay tuned to the MonkeyLearn 101 applications blog series to discover more use cases!