vinayak1998/amazon-rating-prediction

Predicting Amazon ratings based on reviews by Text Classification using the Naive Bayes Algorithm.

Text Classification using naive bayes

Predicting Amazon ratings based on reviews by Text Classification using the Naive Bayes Algorithm.

Name

naive - Run the executable program for Naive Bayes

Synopsis

./naive <part> <tr> <ts> <output>

Description

This program will train naive bayes model using given code on train data, make predictions on test data and write final predictions in given output file.

Options

• PART

  Part i.e. a,b or c

• TR

  File containing training data in csv format where 1st entry is the target

• TS

  File containing test data in csv format where 1st entry is the target

• OUT

  Output file (write your predictions in this file)

Example

./naive a train.csv test.csv output

Parts

• Part A

  • Used Unigrams as features
  • Used Laplace Smoothing to avoid zero probabilities(c = 1)
  • Used Logarithms to avoid underflow issues
  • Implemented everything from the First Principles and not usde any existing R/python modules.

• Part B

  • All in Part A plus some Pre-Processing
  • Stopword Removal(nltk)
  • Stemming(nltk)
  • String Cleaning

• Part B

  • Feature Engineering
  • Lemmatization
  • Used Bigrams as features

Data

• amazon_train.csv: Train data
• amazon_test_public.csv: Public Test data

Note: In the Public test data, actual class labels are replaced with -1