Pathairush / learn_nlp_with_bags_of_popcorn

Practicing NLP (natural language processing) techniques from the IMDB sentiment analysis dataset.

Practice NLP with Bags of Popcorn

Introduction

This repository is a hands-on implementation for the IMDB movie review sentimental analysis. The part of tutorial section is following Bags of Popcorn kaggle competition. It's provided a step-by-step improvement leveraging different NLP techniques with the same dataset. I've extended the tutorial based on my interest such as adding Deep learning model, or Multiple input features.

Learning objectives

• Familiar with the tools

  • nltk
  • gensim
  • keras

• Learn the processeses

  • Data cleansing (Remove, Filtering, Tokenizing, Lemmatization, Padding, etc.)
  • Feature prrocessing (BOW, TFIDF, Feature engieering, Word Embbedding)
  • Input structure required by different models (Tabular, Sequences, Multi-input)
  • Model structure (Tree-based model, Deel learning, Recurrent NN, etc.)

• Applied to others

  • Binary classification (sentimental analysis)
  • Multi-class classification (types of review)
  • Regression (polarization score)
  • Applied text features with other kinds of problem (propensity, churn, etc.)

Roadmap

• Try more feature engieering techniques from text data

  • Part of speech
  • Verbal and grammatical
  • Uni | Bi | Tri gram
  • LDA clustering

• Applied to other problems

  • Natural language Processing with diaster tweets
  • Jigsaw Unintended Bias in Toxicity Classification