Portfolio Project: Predict Rating given Amazon Product Reviews using NLP

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Section 1: Introduction

  1. Understanding the Dataset
  2. Download Resources
  3. Problem objective and framing
  4. Packages used
  5. Load data and fix the scores

Section 2: Exploratory Data Analysis (EDA) and Theory

  1. Data Cleaning - Fix duplicates
  2. Why convert text to a vector
  3. Bag of words model
  4. Similarity
  5. Disadvantages of BoW
  6. Binary Bag of Words

Section 3: Text Processing

  1. Part 1 - Stop words
  2. Part 2 - Why make it lower case
  3. Part 3 - Stemming and Lemmatization
  4. Code Demo - Preprocessing Review Text Data
  5. Code Demo - Preprocessing Summary Text Data

Section 4: Feature Engineering

  1. Code Demo - Bag of Words
  2. Unigram, Bigrams and Trigrams
  3. Code Demo - Create Bigrams and Trigrams

Section 5: TFIDF

  1. What is TF-IDF and why.
  2. Why use log in TFIDF.
  3. Code Demo - TFIDF

Section 6: Word2Vec

  1. Introduction to Word2Vec
  2. Code Demo - Training Word2Vec
  3. Averaging Word2Vec
  4. TFIDF weighted Word2Vec
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