Unicorn NLP develops dedicated technology for travel reviews.
More profound than Watson and more accurate than Google in the travel domain.
It is also much more cost-effective and you can get the on-premise version with 0 cents / text.
It is the next-generation of Keywords, Categories, Concepts and Sentiment Analysis.
Our technology detects more high-quality information from reviews than these services combined.
We will be happy to help you implement your brilliant ideas and discover what is possible.
Each product (AI-in-the-box) is a set of 120+ precise Semantic Models designed for a specific travel domain (Hotels, Hostels, Apartments, Restaurants, etc.).
With this unique approach, we cover more than 90% of information/facts contained within the review.
Testing it on hundreds of thousands of reviews, we achieved very high accuracy (precision=95%)
We call it Human-like Analysis because with these parameters we achieve the same quality as detected by skilled analysts (humans). It opens up a lot of new functionalities that can be easily combined with your product without any post-processing, additional training or configuration to your data.
With the right technology you are able to provide deep AI that even a child can understand
and much more. Click below and see examples of how our tech can be applied to the travel domain.See more about what this technology can do
You get ready-to-use technology that you can integrate with your product in days.
You can use this technology as a standalone without any linguistic or machine learning skills, or augment your already functioning machine learning solutions.
You do not get a "domain-independent" solution that sometimes works, sometimes doesn't and it is up to you to configure it to your data. We specialize in making deep AI for specific texts which are easy to use. We hide all the complexity inside and provide ready-to-use data on the output in a well-organized JSON.
Each product was designed, built and tested on hundreds of thousands of reviews and other texts from a specific domain. You do not need to set any confidence score, apply any post-processing or train these models to your data. We undertake to ensure that our technology works at a very high level with your data.
All information detected by our technology makes sense and is simple and accurate enough to display to the user or used to provide new functionalities.
|"Breakfast was tasty"||=>||Breakfast: positive|
|"Breakfast was huge"||=>||Breakfast: positive|
|"Breakfast was not free"||=>||Breakfast: negative|
|"Breakfast was very tasty but really small"||=>||Breakfast: neutral|
|"Breakfast was huge but you had to pa extra"||=>||Breakfast: neutral|
|"Breakfast was tasty"||=>||Breakfast: tasty|
|"Breakfast was huge"||=>||Breakfast: big portions|
|"Breakfast was not free"||=>||Breakfast: free|
|"Breakfast was very tasty but really small"||=>||Breakfast: tasty | small portions|
|"Breakfast was huge but you had to pay extra"||=>||Breakfast: big portions | not free|
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Read Article: How to make AI personalization based on reviews
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