pranshu1921 / Amazon-Fine-Food-Reviews

Using NLP to determine user review polarity on Amazon.

Amazon-Food-Reviews-Analysis-and-Modelling Using Various Machine Learning Models

Performed Exploratory Data Analysis, Data Cleaning, Data Visualization and Text Featurization(BOW, tfidf, Word2Vec). Build several ML models like KNN, Naive Bayes, Logistic Regression, SVM, Random Forest, GBDT, LSTM(RNNs) etc.

Objective:

Given a text review, determine the sentiment of the review whether its positive or negative.

Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews

About Dataset

The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.

Number of reviews: 568,454
Number of users: 256,059
Number of products: 74,258
Timespan: Oct 1999 - Oct 2012
Number of Attributes/Columns in data: 10

Attribute Information:

1. Id
2. ProductId - unique identifier for the product
3. UserId - unqiue identifier for the user
4. ProfileName
5. HelpfulnessNumerator - number of users who found the review helpful
6. HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not
7. Score - rating between 1 and 5
8. Time - timestamp for the review
9. Summary - brief summary of the review
10. Text - text of the review

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Amazon Food Reviews EDA, NLP, Text Preprocessing and Visualization using TSNE

1. Defined Problem Statement
2. Performed Exploratory Data Analysis(EDA) on Amazon Fine Food Reviews Dataset plotted Word Clouds, Distplots, Histograms, etc.
3. Performed Data Cleaning & Data Preprocessing by removing unneccesary and duplicates rows and for text reviews removed html tags, punctuations, Stopwords and Stemmed the words using Porter Stemmer
4. Documented the concepts clearly
5. Plotted TSNE plots for Different Featurization of Data viz. BOW(uni-gram), tfidf, Avg-Word2Vec and tf-idf-Word2Vec

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