NLP Project for SDAIA T5 Data Science Bootcamp. This project consists of sentiment analysis for hotel reviews and classification algorithms based on that. Also, the project has word clustering models and a hotel recpmmendation system based on the nationalities and the reviewers' scores.

No description available

This repository details a Sentiment Analysis project designed to classify hotel reviews based on text content using traditional Machine Learning techniques in Python. The pipeline covers data preprocessing, TF-IDF vectorization, and training classification models (Logistic Regression and Random Forest).

TripAdvisor web scrapping, text classification with ML algorithms and sentiment analysis with NLP.

Currently pursuing a Study Oriented Project under Prof Yashwardhan Sir , Topic : Conversational AI for understanding Customer Behavior; We are a team of 4 and the project is aimed at building a multilingual chatbot in association with C-Zentrix; Role: Sentiment analysis , Fine-tuned BERT Model for Binary Sentiment analysis, implemented multi class-sentiment analysis on hotel review dataset, implemented an LSTM model for binary Sentiment Classification

This is a Capstone Project materials done during DSI. Capstone Project is a classification project using Hotel Reviews and obtain Sentiment Analysis.

This project enhances a local hotel's online reputation through text classification and sentiment analysis of Google reviews. Using SerpAPI for web scraping, it performs thematic analysis of low-rated reviews and develops a predictive model to correlate guest expenditures with review sentiments.

Sentiment analysis and classification of hotel reviews

No description available

No description available

Hotel Review Classification Using Sentiment Analysis and Machine Learning

Classification of hotel reviews on positive and negative class by using sentiment analysis

Sentiment analysis and review classification of hotel customer feedback using NLP techniques in Python

NLP preprocessing pipeline for hotel reviews — includes cleaning, tokenization, stopword removal, lemmatization, n-gram analysis, and sentiment classification with VADER.

Comparative sentiment analysis on hotel reviews using Logistic Regression, Random Forest, and Bidirectional LSTM to evaluate classification performance in NLP.

A sophisticated NLP pipeline that extracts key sentences from multi-sentence hotel reviews and performs sentiment classification using advanced text processing techniques and VADER sentiment analysis.

Predict hotel review sentiment and predict star ratings using fine-tuned RoBERTa with ordinal classification and embedding-based analysis.

Sentiment analysis on TripAdvisor hotel reviews using TF-IDF and BERT embeddings. Includes supervised classification (RF, SVM, FFNN) and model interpretability with LIME.

Sentiment Analysis on customer reviews of a hotel dataset using the NLTK toolkit and text classification, language translation and entity extraction in NLP

hotel reviews prediction using regression and classification dataset . apply preprocessing , feature selection, feature engineering, PCA , label encoding, sentiment analysis, standard scaler , data visualization,

🏨 Full-stack hotel review sentiment analysis web app with React frontend and FastAPI backend. Uses DistilBERT AI model for real-time sentiment classification of hotel reviews. Features modern UI, RESTful API, SQLite database, and comprehensive analytics dashboard.

Sentiment analysis of Hotel UNY guest reviews using Machine Learning. This project focuses on text preprocessing and classification to gain insights into customer satisfaction.

This project enhances a local hotel's online reputation through text classification and sentiment analysis of Google reviews. Using SerpAPI for web scraping, it performs thematic analysis of low-rated reviews and develops a predictive model to correlate guest expenditures with review sentiments.

Sentiment analysis of Arabic hotel feedback using AraBERT. This project leverages AraBERT, a transformer-based model for Arabic text, to classify customer sentiment in hotel reviews. The repository includes the code, training pipeline, and pre-trained model for sentiment classification tasks.

The project is about the area of natural language processing. Using NLP to make sentiment analysis on the hotel reviews and apply classification models to predict the review rate.

Sentiment analysis of TripAdvisor hotel reviews using classical ML, BERT embeddings, topic modeling, and aspect-based classification. Includes full preprocessing pipeline, majority voting for ground truth, and performance evaluation.

This project performs sentiment analysis on Indonesian Google Maps reviews of 3-star hotels in Jakarta using an IndoBERT-based classification model with hyperparameter tuning. The predicted sentiments are aggregated into a sentiment score to rank and recommend hotels, helping travelers make data-driven accommodation decisions.

This project performs sentiment analysis on hotel guest reviews using NLP techniques. It includes data preprocessing, sentiment classification, aspect extraction, model training in Jupyter Notebook, and a Flask-based dashboard for visualizing results. The goal is to help hotels understand customer feedback and improve service quality.

NLP pipeline for analyzing hotel review sentiment from TripAdvisor data. Implements text preprocessing (tokenization, stemming, lemmatization), sentiment classification (positive/neutral/negative), and frequency analysis of key terms. Extracts actionable insights from user-generated content to identify hotel satisfaction patterns.

NLP Project for SDAIA T5 Data Science Bootcamp. This project consists of sentiment analysis for hotel reviews and classification algorithms based on that. Also, the project has word clustering models and a hotel recpmmendation system based on the nationalities and the reviewers' scores.