Products -> Custom-made AI In-the-Box for Any Reviews/Texts
This is a custom-made order detecting any information from any Reviews/Texts. We can analyze your data and create the list of Semantic Models ourselves or you can tell us what would you like to garner from your data. Leadtime is 1-3 months (depending on your data and what information you want to garner) and we give you ready-to-use API trained&tested on your data.
Clasically, each product consist of 120+ Semantic model that was especially designed, built, tested, and re-tested on hundreds of thousands of accommodation reviews from 10 different sources. All presented Semantic Models work with an unparalleled precision of 90-95%.
We do all the work so the technology prepared for you conforms to our demanding standards.
For most travel reviews, we garner this type of information.
Will come back? vs. not come back?
Overall good/satisfied. vs. Disappointed?
Place's General Opinions
Is this place clean? Dirty? Renovated? Outdated?
Long waiting lines? Crowded place?
Need a reservation? Reservation problems?
Critical (dangerous to the brand) opinions
Place was not as advertised?
Hiden fees that people complains about?
Dirty place? Service rude?
Nice vibe? Cozy, relaxing atmosphere? Pleasant? Unpleasant vibe? Weird people?
Not welcoming? The decor was nice? Interior outdated?
Was it overpriced? Cheap, expensive? Was it worth them money?
Is there a service charge? Any additional fees? Bill problems?
Only cash payment?
Will recommend? vs. not recommend?
Is it good for: Kids? Family? Couples?
Staff was friendly? Professional? Unpleasant?
Waiting time was acceptable? Long lines?
Service was quick, slow? good?
Wifi - is it free? Is it fast?
Does parking have lots of spaces? limited? lack of parking?
Is suitable for a wheelchair? Pet-friendly/unfriendly?
For an overview of what this technology can do, explore our existing products for hotel, hostel, apartment and restaurant reviews.
We present it on a hotel review (AI In-the-Box for Hotel Reviews):
Simplified output after processing by AI In-the-Box for Hotel Reviews:
|1.||The hotel was clean and renovated, service was friendly too||
staff helpful, friendly +2
hotel renovated +1
|2.||But thats it|
|3.||The bathroom was dirty, Shower got poor pressure||
shower problem -2
bathroom dirty -2
|4.||I think there were fleas in our bed||
|5.||The pool was closed during our stay and staff couldn't tell us where it will be open and does not seem to care||
staff unfriendly, unhelpful -2
|6.||The breakfast was a joke, the portions was small and we were told that we could pay to get more, so it wasn't free as promised||
poor, limited -1
breakfast not free as promised -2
breakfast bad -1
|7.||Wanted to get my money back but they refused, not coming back||
will not return -2
payment refound problem -1
We specialize in creating dedicated Natural Language Understanding engine for specific reviews. We focused in travel and prepared a set of products to process many different travel reviews (e.g. hotel, hostel, apartment, restaurant, airline reviews).
We stand by the statement that we detect more information than Watson or than any other Deep Learning solution and we are more accurate than Google in Travel Domain. We are also much more cost-effective. And we allow the processing of as much data as you want with no additional cost (0 cents / text).
This is possible because we created the next-generaion process of developing NLU systems in which we can build Semantic Models 10 x faster. It is designed from the ground up to process reviews, not proper english texts. It is the result of 12 years of experience in NLP/NLU and 8 years in processing reviews, and more than 50 big implementations (2007-2019) of NLU systems to process reviews and other user generated content.
We are passionate and proud of our technology and aim to provide the optimal technology for travel reviews. We will be happy to help you implement your brilliant ideas and discover what is possible.
|"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 pay 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|
Language has colors. Do not reduce it to Black & White.
Technological differences (compared to other NLP/NLU solutions):
The semantic model is the newest approach to an information extraction models where boundaries of those models are not determined by the structure of a language (or grammar, or used words), but the type of information you are detecting. It does not matter how the user writes an opinion about a specific object, e.g. “breakfast was a joke”, “Muffin and cold coffee is not a breakfast”, “Breakfast wasn’t included as written”, our semantic model still detects it and provides structured-data. With "breakfast" example, it is relatively easy to provide a shallow analysis (e.g. Sentiment Analysis) with the current NLP techniques. If you are looking for more sophisticated information (e.g. is it safe?, is it handicap friendly?, will someone come back?, Is it sustainable? Is it pet-friendly?) the situation gets more complicated. Current techniques provide a part of this information and their accuracy relies on the keywords used in reviews. Users are very creative in the reviews and they are not using a keyword approach. Let’s take one example. If we want to answer the question “is it safe?”, you need to capture a lot of information from reviews without common keywords safe/dangerous. In practice they write this in tens or hundreds of ways: e.g. “there was a lot of drunken people outside”, “I did not feel good because of the neighbors”, “someone yelled in the night and the front desk lady did nothing”. We believe, that if the human can understand what is written in the review, then technology should detect it as well. That is why we call them Semantic Models, not information extraction models.
Our UnicornNLP Semantic Environment is a place where we design and precisely craft all the semantic models. It is a perfect combination of human and computer intelligence. It is the secret sauce of our technology and it allows us to deliver the best technology in the world for reviews. Statistical algorithms provide the data based on the processing of hundreds of thousands of reviews, but the humans are making all the crucial decisions. Given the pace of AI development, this is significant advantage in information extraction (in NLP/NLU) and it will be appropriate for the next decade. Think about our environment as a way of creating tens of thousands of very precise, hand-crafted semantic rules but with an AI as an assistant that is generating new ideas. These ideas are provided by an AI assistant based on constant simulations on real data and statistical tools.
We designed a semantic programming language (QL4Reviews) to extract information from reviews. With this programming language, you build semantic models 10 times faster than in other current technologies. Every Semantic Model is programmed in QL4Reviews and consist of 50-2000 lines of semantic code. Heavily optimized engine allow to process through 124 Semantic Models on 1 low-cpu Amazon AWS instance - 1 million reviews a day. Speed of the core engine was very important for us from the beginning.
Our technology is designed from the ground up to process reviews, not proper-grammar texts. It can process also machine-translated reviews/texts (via Google Translate or Microsoft Translate). This approach provides almost as good results as processing English reviews but we do not have to rewrite deep semantic models into other languages. It is much easier to maintain and scale (because there is only 1 engine).
Read more & See demo: Processing machine-translated non-English Travel Reviews
This is not just another NLP API/Tool. We redesigned every layer of technology and we wanted to address the biggest challenge in the NLP/NLU industry. We wanted to change the way you use and think about Natural Language Understanding API. In our opinion, if you are using NLP API, you should not get your hands dirty and spend inordinate time thinking how to best implement this API into your application. We wanted to hide all the NLP complexity inside the system and provide you ready-to-use data. We believe that NLP API should be simple and easy to use without linguistic, machine learning or other expertise. You do not need to spend weeks to improve the accuracy of "domain-indepentent" NLP API, to adjust to Your domain or to make decisions about complex output parameters. Getting output parameters like confidency (e.g. 0.7852) or polarity (e.g. 0.4352) or keywords detected (e.g. friendly, 0.6786; dissapointed, -0.7564) gives every developer problems and frankly they are not standard or predictable. They are rather a consequence of used technology inside, and putting these parameters for the output in our opinion is the way to leave configuration and responsibilty for decision-making to You. We believe in human-like output where a human can understand what this API detected. We believe that we have developed excellent API for specific domains, so you can get tailored results and focus on your business. We position ourselfes as a read-to-use technology, not a standard API that is a tool that you need to configure and train to your data.
If you want to buy a Product, receive more info about the On-Premise version (AI In-the-Box) and Plans&Pricing
or want to discuss what is possible
If you want to test it or see how it works on your data, send us a dataset
(we do not collect your data)
You can also start with the SaaS version - pay per text (hosted via rapidAPI & Amazon AWS)
before you get the On-Premise version with 0 cents / text!