The code in this repository can be modified to be compatible with other data sets too.

We use and compare various different methods for sentiment analysis on comments (a binary classification problem). The training dataset is expected to be a csv file of type comment_id,class,content where the comment_id is a unique integer identifying the comment, class is either 1 (positive) or 0 (negative), and content is the comment enclosed in "". Similarly, the test dataset is a csv file of type comment_id,content. Please note that csv headers are not expected and should be removed from the training and test datasets.

There are some general library requirements for the project and some which are specific to individual methods. The general requirements are as follows.

• numpy
• scikit-learn
• scipy
• nltk

The library requirements specific to some methods are:

• keras with TensorFlow backend for Logistic Regression, MLP, RNN (LSTM), and CNN.
• xgboost for XGBoost.

1. Run preprocess.py <raw-csv-path> on both train and test data. This will generate a preprocessed version of the dataset.
2. Run stats.py <preprocessed-csv-path> where <preprocessed-csv-path> is the path of csv generated from preprocess.py. This gives general statistical information about the dataset and will two pickle files which are the frequency distribution of unigrams and bigrams in the training dataset.

After the above steps, you should have four files in total: <preprocessed-train-csv>, <preprocessed-test-csv>, <freqdist>, and <freqdist-bi> which are preprocessed train dataset, preprocessed test dataset, frequency distribution of unigrams and frequency distribution of bigrams respectively.

For all the methods that follow, change the values of TRAIN_PROCESSED_FILE, TEST_PROCESSED_FILE, FREQ_DIST_FILE, and BI_FREQ_DIST_FILE to your own paths in the respective files. Wherever applicable, values of USE_BIGRAMS and FEAT_TYPE can be changed to obtain results using different types of features as described in report.

3. Run baseline.py. With TRAIN = True it will show the accuracy results on training dataset.

4. Run naivebayes.py. With TRAIN = True it will show the accuracy results on 10% validation dataset.

5. Run logistic.py to run logistic regression model OR run maxent-nltk.py <> to run MaxEnt model of NLTK. With TRAIN = True it will show the accuracy results on 10% validation dataset.

6. Run decisiontree.py. With TRAIN = True it will show the accuracy results on 10% validation dataset.

7. Run randomforest.py. With TRAIN = True it will show the accuracy results on 10% validation dataset.

8. Run xgboost.py. With TRAIN = True it will show the accuracy results on 10% validation dataset.

9. Run svm.py. With TRAIN = True it will show the accuracy results on 10% validation dataset.

10. Run neuralnet.py. Will validate using 10% data and save the best model to best_mlp_model.h5.

11. Run lstm.py. Will validate using 10% data and save models for each epock in ./models/. (Please make sure this directory exists before running lstm.py).

12. Run cnn.py. This will run the 4-Conv-NN (4 conv layers neural network) model as described in the report. To run other versions of CNN, just comment or remove the lines where Conv layers are added. Will validate using 10% data and save models for each epoch in ./models/. (Please make sure this directory exists before running cnn.py).

13. To extract penultimate layer features for the training dataset, run extract-cnn-feats.py <saved-model>. This will generate 3 files, train-feats.npy, train-labels.txt and test-feats.npy.
14. Run cnn-feats-svm.py which uses files from the previous step to perform SVM classification on features extracted from CNN model.

• dataset/positive-words.txt: List of positive words.
• dataset/negative-words.txt: List of negative words.
• dataset/glove-seeds.txt: GloVe words vectors from StanfordNLP which match our dataset for seeding word embeddings.