ANGUIDES
Project Description
We analysed human sentiment through Natural Language Processing (NLP) and Machine Learning (ML) to propose a hybrid software framework (HSF) that will automatically fulfil people’s needs and
Latent Dirichlet Allocation (LDA) techniques are used to analyse the sentiment regarding tourism, including locations, costs, hotels, the nearest food, etc. Positive and negative opinions were classified using the Naive Bayes (NB), Logistic Regressio...
Our Overview
Machine Learning
The HSF is connected to four significant components: The user Input Layer (UIL), Input Communication Layer (ICL), Recommendation Layer (RL), and Payment Gateway Module (PGM). The UIL module is intended to maintain engagement between the end user and the scheme, as well as propose news and articles regarding carbon emissions to raise the end user’s awareness of their carbon footprint. The RL component is designed through the Collaborative Filtering Method (CFM). We have applied the K-Nearest Algorithm (KNN),
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To Reduce Carbon Emissions
To Reduce Carbon Emissions
Our proposed green eco-tourism model will suggest how to reduce carbon emissions from traveling. Furthermore, our system will suggest a cost-effective hotel with affordable prices and solve all travelers’ queries. In addition to these, sentiment analysis from the hotel review also utilized the most dominant words in our study to understand any traveler’s sentiments better. Our research aims to build a green eco- system-based application that eases the inconvenience of trav- eling worldwide with the help of hotel booking, cost, nearest location, meals, ticket booking, and tourist guide.
