Sentiment-Classification

Assignment 1.1 - COL772(Spring'19): Natural Language Processing

Creator: Navreet Kaur[2015TT10917]

Problem Statement:

The goal of the assignment is to build a sentiment categorization system for business reviews from annotated data. The input of the code will be a set of annotated business reviews from the website Yelp.

Training Data:

Training dataset of business reviews can be downloaded from this link: https://owncloud.iitd.ac.in/nextcloud/index.php/s/2KRyxd9XLFcnpXR. Each data point has review text, and a rating. Ratings are floating point numbers between 1 and 5.

The Task:

Build a non-neural classifier that given a review, predicts its sentiment polarity.

Files:

1. knowledge.py: Containes of in-domain stopwords, positive and negative words etc.
2. preprocess.py: Methods for preprocessing the raw data.
3. features.py: Methods for extracting different features from the pre-processed data.
4. form_matrix.py: Methods to form feature matrix from words and the features formed using previous script.
5. predict.py: Methods for building and training the model given feature and label matrix.
6. variables.py: Global tunable variables for selecting the preprocessing steps, features to use, vocabulary building method, model and other hyperparameters.
7. train.py: Main file to run for training the model given the data.
8. test.py: For making predictions on test data.
9. compile.sh: Compiling the whole code.
10. train.sh: Run sh ./train.sh trainfile.json devfile.json model_file to train the model with trainfile.json as training data and devfile.json as validation data. Model will be saved in model_file
11. test.sh: Run ./test.sh model_file testfile.json outputfile.txt to test the trained model on testfile.json. A new file outputfile.txtcontaining a sequence of numbers between 1 and 5, representing the predictions, will created.