Travel industry uses sentiment analysis to understand customers and improve their experience. Brand monitoring, competitive research, product analysis – these are just several ways OTAs and hotels apply it. The AARCHIK data science team considered a traveler-facing spin on this technology
Sentiment Analysis For Hotel Amenities
Overview
Sentiment Analysis For Hotel Amenities
Overview
Travel industry uses sentiment analysis to understand customers and improve their experience. Brand monitoring, competitive research, product analysis – these are just several ways OTAs and hotels apply it. The AARCHIK data science team considered a traveler-facing spin on this technology
Business Need
The sentiment analysis framework uses natural language processing (NLP) and can help travelers get the most detailed information about a hotel, check amenities ratings, and compare hotels based on these parameters. The tool aggregates customer-created hotel reviews from public sources, analyzes them, and then generates amenity quality ratings for each hotel.
The framework started as an internal proof of concept, is now ready for integration into travel review platforms and online travel agencies that distribute hotels as a plug and play module
Scope
New Product
Industries
Travel and Tourism
Services
Strategy & Consulting
Technologies
Angular JS
Node JS
Figma
Python
Tableau
Solution Provided
01
Defining hotel amenities comparison criteria and the overall product concept
02
Preparing a dataset with labeled sentiment for amenities
03
Training a model to score and analyze the reviews by amenities
04
Creating a user-friendly interface
Solution Provided
01
Defining hotel amenities comparison criteria and the overall product concept
02
Preparing a dataset with labeled sentiment for amenities
03
Training a model to score and analyze the reviews by amenities
04
Creating a user-friendly interface
Challenges
01
Initially getting training data sets were a challenge
02
Research was needed for accurate mathematical and statistical models for the analysis
03
Appropriate NLP frameworks were needed to fit the mathematical and statistical models
04
Front End Frameworks for building the appropriate UI
05
Micro services Architecture For Plug and Play Modules
Benefits
The project’s scope was 8 man-months. The product was completed over the course of 2 months by a team of five professionals: 2 machine learning engineers, a software engineer, a UI/UX designer, and a project manager.
The technology stack of the project included Python, NLTK, Keras, and TensorFlow (for the algorithms), HTML and JavaScript (for the user interface).
Benefits
The project’s scope was 8 man-months. The product was completed over the course of 2 months by a team of five professionals: 2 machine learning engineers, a software engineer, a UI/UX designer, and a project manager.
The technology stack of the project included Python, NLTK, Keras, and TensorFlow (for the algorithms), HTML and JavaScript (for the user interface).
Have A Query?
Do you need a detailed consultation or feasibility study for this topic? Or it maybe you just want to exchange views and thoughts on this topic. Do get in touch with us and we would be glad to share a cup of coffee together and discuss this topic together.
Have A Query?
Do you need a detailed consultation or feasibility study for this topic? Or it maybe you just want to exchange views and thoughts on this topic. Do get in touch with us and we would be glad to share a cup of coffee together and discuss this topic together.
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