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Experiment #102

Hotel Customer Reviews Classification




The Experiment #102 researches about the possibilities of applying Cognitive Services and Deep Learning to understand and manage a huge amount of information from human interactions without the human intervention. We will discover if the Artificial Intelligence is capable of collect all this raw data, classify it and manage all the different situations.



The project tries to simulate an automatic classification of comments or reviews in a business environment. Any company wants to identify critical requests as soon as possible to try to solve it before them become a big problem, regardless of the number of messages that arrive, or the language in which them were wrote, or the time that them were sent.



For this simulation it is been chosen the case of an hotel. We will imagine that the customers have access to a mobile application in which they can leave their comments, complaints and suggestions. They can use images to illustrate their point of view or location and text messages to explain it. Due to content, the reviews will be classified as good, neutral or bad.
In this case we want to classify each comment and identify the bad, urgent and important ones to manage them quickly and, if it were necessary, report to a different human agent depends of the location (room, bar, restaurant, swimming pool, etc, who will can solve it properly.



First, we are going to create an Azure Notebooks project where we can set up a Notebook server. Then we will create and upload some files based on Python to deploy a Text Summarization service in an Azure Machine Learning Workspace. After that, we will construct and train a simple deep neural network classification model, with Keras and Tensorflow, that will classify the hotel customers reviews.
Also, we will perform the integration with the Azure Cognitive Services along with the Azure Machine Learning. We will use Computer Vision API to collect information from pictures or photographs and Text Analytics API to extract data from human utterances.



The principal advantage of using Cognitive Services and Deep Learning in this case is the possibility of identifying very quickly the reviews or comments that needs to be carefully manage by a human been because of its importance or its urgency.
Even if tons and tons of messages arrive, we can be sure that the most critical messages will be identified and treated immediately and the rest of them will be classified and stored correctly.
This way a company will save money and resources and will be very much efficient attending customers’ requests.

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