基于50万亚马逊美食评论数据集的评论分类系统 Review classification system based on 500 thousand Amazon gourmet review data
This Project includes sentiment analysis of Customer Product Reviews that can be implemented using the Machine Learning and Python
Labeled data for cross-source cross-domain sentiment classification
Sentiment analysis using NLP techniques on Amazon product reviews. It covers text pre-processing, visualization, and basic sentiment classification.
This project crawls Amazon reviews and extracts features and opinions to calculate a feature based rating of every product (mainly smartphones) Done with python, pyqt5
Text Classification Problem : Wrote a module to classify Amazon-Product Reviews as favourable/unfavourable. Achieved accuracy of 78% and an F1 score of .81 using Logistic Regression on a test-train split of 20%, where total records were around 50000.
Logistic Regression to predict the helpfulness score (0 or 1) of amazon review i.e. text data
Test the genuinity of reviews.
Built a recommender system using Apache Mahout machine learning library carried out data analysis using Hadoop, Apache Hive & Pig on Amazon Customer Reviews Data set(130M+ reviews))
This Project - "Amazon Review Analysis" leverages machine learning and natural language processing techniques to perform sentiment classification on Amazon customer reviews. It provides a web-based interface and an API for seamless integration, enabling efficient sentiment prediction and analysis.
In this assignment I will put my ETL skills to the test. Many of Amazon's shoppers depend on product reviews to make a purchase. Amazon makes these datasets publicly available. However, they are quite large and can exceed the capacity of local machines to handle. One dataset alone contains over 1.5 million rows; with over 40 datasets, this can b…
Sentiment Analysis on Amazon 2018 customer review dataset
An end-to-end semantic search pipeline on 568K+ Amazon reviews: TF-IDF baseline, Sentence-Transformer embeddings, a scalable FAISS index, UMAP visualization, and an honest look at where embeddings fail. Built as both a reusable reference and a teaching notebook.
Amazon reviews extract/scrape by using BeautifulSoup, Splash JS, Docker, Python.
This project demonstrates how to perform sentiment analysis using deep learning on Amazon product reviews dataset. The dataset used for the project is obtained from Kaggle and consists of nearly 3000 reviews of amazon users regarding various amazon Alexa products like Alexa echo, Alexa dot etc. Exploratory data analysis is performed on the datas…
Sentiment analysis using different types of Bidirectional Recurrent Neural Networks on Amazon reviews dataset. The results are confronted with two baseline models which are an SVM and a RF model.
The scraper program build on Python to get all reviews under all products of a search keyword. Use chrome to work
📚 Performed sentiment analysis on Amazon Kindle reviews using BoW, TF-IDF & Word2Vec. 📊 Analyzed model performance and insights from review patterns.
Amazon-Fine-Food-Reviews
NLP pipeline for Amazon reviews — embeddings, vector DB, semantic search, clustering, RAG triage
To associate your repository with the amazonreviews topic, visit your repo's landing page and select "manage topics."