amazonreviews

There are 0 repository under amazonreviews topic.

• abdulmoiz37 / Sentiment-Analysis-of-Customer-Reviews-using-machine-learning

  This Project includes sentiment analysis of Customer Product Reviews that can be implemented using the Machine Learning and Python

  amazonreviewsmachine-learningscikit-learnsentiment-analysissentiment-classificationtwitteranalysisreviewsaccuracy
  Language:Python 3

• huangyueranbbc / ClassificationModelForAmazonReviewsDemo

  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

  classificationdeep-learningsparkhadoopamazonreviewsamazon-foodclassification-treeslogistic-regressionbig-data
  Language:Java 3

• paolazola / Cross-source-cross-domain-sentiment-analysis

  Labeled data for cross-source cross-domain sentiment classification

  amazonreviewsdomainadaptationfacebookposttripadvisorreview
  3

• anoushka1196 / FeatureX-Amazon-Feature-Opinion-Mining

  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

  python-3-6pyqt5pyqt-guipyqt5-desktop-applicationcrawlamazonsummarizationfeature-extractiondesktop-applicationfeatureopinion-miningamazonreviews
  Language:HTML 2

• abhinavthapper31 / Sentiment-Analysis-on-Product-Reviews

  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.

  text-classificationlogistic-regressionf1-scoreamazonreviewsaccuracynlpbinaryclassification
  Language:Python 1

• letus21500 / Amazon-Electronic-Reviews

  Test the genuinity of reviews.

  amazonreviewspythonstreamlitmachine-learning
  Language:Jupyter Notebook 1

• sayaliwalke30 / BigDataAnalysis-RecommenderForAmazon

  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))

  hadoophadoop-mapreducemahoutemrdata-analysisbigdatasetamazon-s3amazonemr-clustermap-reducealgorithmsamazonreviews
  1

• bhattji007 / amazon_product_reviews_analysis

  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.

  amazonreviewdatasetamazonreviewsnatural-language-processingnlpsentiment-analysis
  Language:Jupyter Notebook 0

• Elliott-dev / Big-Data-Challenge--Amazon-Shoppers-Product-Reviews

  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 be quite taxing on the average local computer. My first goal for this project will be to perform the ETL process completely in the cloud and upload a DataFrame to an RDS instance. The second goal will be to use PySpark or SQL to perform a statistical analysis of selected data.

  amazonrdsamazonreviewdatasetamazonreviewsawsaws-ec2aws-s3googlecolaboratorypysparkspark-sqlsql
  Language:Jupyter Notebook 0

• hridayashinde / Amazon-Review-Helpfulness-Predictor

  Logistic Regression to predict the helpfulness score (0 or 1) of amazon review i.e. text data

  nlpnlp-machine-learningamazonreviews
  Language:Jupyter Notebook 0

• roshancyriacmathew / Deep-Learning-on-Amazon-Product-Reviews

  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 dataset to analyse various columns and the data is visualized using count plots and pie charts. The reviews are then processed using various methods which involve lowercase conversion, URL removal, punctuation removal, tokenization, stop word removal and stemming. The processed data is then separated into positive and negative reviews and are then visualized using Word clouds, as word clouds help to identify the most prominent/frequently used words. The processed data is then passed into a neural network, where the network learns from the data. The accuracy of the model is then measured by running the model on the test data.

  amazonamazonreviewdatasetamazonreviewsdeep-learningdeep-neural-networkskagglekaggle-datasetneural-networkneural-networkssentiment-analysistext-classificationtext-miningyoutubethe-ai-and-ds-channel
  Language:Jupyter Notebook 0

• Saurabhkg03 / Amazon-reviews-extract

  Amazon reviews extract/scrape by using BeautifulSoup, Splash JS, Docker, Python.

  amazonamazonreviewsbeautifulsoupdata-sciencedockerpythonscrapingsplashwebscraping
  Language:Python 0

• giacoballoccu / DLA-SentimentAnalysis

  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.

  svmdla-sentimentanalysisdatasetamazonreviewsrandom-forestbidirectional-lstmbidirectional-gru
  Language:Jupyter Notebook

• suvajit790 / RNN-assignment

  Sentiment Analysis on Amazon 2018 customer review dataset

  rnn-tensorflowlstm-neural-networkstensorflowamazonamazonreviewdatasetamazonreviewssentiment-analysisrnn
  Language:Jupyter Notebook

• teja0508 / Amazon-Fine-Food-Reviews-

  Amazon-Fine-Food-Reviews

  amazon-fine-food-reviewssentiment-analysisamazonreviews
  Language:Jupyter Notebook