Labeled data for cross-source cross-domain sentiment classification
-
Updated
Sep 23, 2019
#
amazonreviews
Here are 15 public repositories matching this topic...
This Project includes sentiment analysis of Customer Product Reviews that can be implemented using the Machine Learning and Python
machine-learning
sentiment-analysis
scikit-learn
sentiment-classification
twitteranalysis
amazonreviews
reviewsaccuracy
-
Updated
Aug 25, 2022 - Python
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
big-data
spark
deep-learning
hadoop
classification
logistic-regression
classification-trees
amazon-food
amazonreviews
-
Updated
Aug 22, 2017 - Java
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
amazon
pyqt5
python-3-6
desktop-application
feature-extraction
crawl
summarization
opinion-mining
feature
pyqt-gui
amazonreviews
pyqt5-desktop-application
-
Updated
Oct 24, 2022 - HTML
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.
-
Updated
Jul 15, 2019 - Python
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))
emr
hadoop
algorithms
amazon
map-reduce
dataset
mahout
data-analysis
big
hadoop-mapreduce
amazon-s3
emr-cluster
amazonreviews
-
Updated
Apr 13, 2020
Test the genuinity of reviews.
-
Updated
Mar 26, 2021 - Jupyter Notebook
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.
-
Updated
Apr 15, 2021 - Jupyter Notebook
Amazon reviews extract/scrape by using BeautifulSoup, Splash JS, Docker, Python.
-
Updated
Oct 20, 2023 - Python
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…
aws
sql
aws-s3
pyspark
aws-ec2
spark-sql
amazonreviews
amazonrds
amazonreviewdataset
googlecolaboratory
-
Updated
Jun 15, 2022 - Jupyter Notebook
Sentiment Analysis on Amazon 2018 customer review dataset
sentiment-analysis
amazon
tensorflow
rnn
rnn-tensorflow
lstm-neural-networks
amazonreviews
amazonreviewdataset
-
Updated
Dec 19, 2021 - Jupyter Notebook
Logistic Regression to predict the helpfulness score (0 or 1) of amazon review i.e. text data
-
Updated
Nov 15, 2021 - Jupyter Notebook
This GitHub repository showcases sentiment analysis in Python using VADER and the Roberta Pretrained Model. Explore two powerful techniques for analyzing sentiment in textual data and gain insights into the sentiment behind amazon reviews.
-
Updated
Aug 1, 2022 - Jupyter Notebook
Amazon-Fine-Food-Reviews
-
Updated
Sep 15, 2020 - Jupyter Notebook
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…
text-mining
youtube
deep-neural-networks
deep-learning
sentiment-analysis
neural-network
amazon
text-classification
kaggle
neural-networks
kaggle-dataset
amazonreviews
amazonreviewdataset
the-ai-and-ds-channel
-
Updated
May 31, 2022 - Jupyter Notebook
Improve this page
Add a description, image, and links to the amazonreviews topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the amazonreviews topic, visit your repo's landing page and select "manage topics."