Twitter Sentiment Analysis

Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.
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Sentiment Analysis on Tweets

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Update(21 Sept. 2018): I don't actively maintain this repository. This work was done for a course project and the dataset cannot be released because I don't own the copyright. However, everything in this repository can be easily modified to work with other datasets. I recommend reading the sloppily written project report for this project which can be found in docs/.

Dataset Information

We use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. 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.

Note: It is recommended to use Anaconda distribution of Python.



  1. Run <raw-csv-path> on both train and test data. This will generate a preprocessed version of the dataset.
  2. Run <preprocessed-csv-path> where <preprocessed-csv-path> is the path of csv generated from 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.


  1. Run With TRAIN = True it will show the accuracy results on training dataset.

Naive Bayes

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

Maximum Entropy

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

Decision Tree

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

Random Forest

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


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


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

Multi-Layer Perceptron

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

Reccurent Neural Networks

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

Convolutional Neural Networks

  1. Run 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

Majority Vote Ensemble

  1. To extract penultimate layer features for the training dataset, run <saved-model>. This will generate 3 files, train-feats.npy, train-labels.txt and test-feats.npy.
  2. Run which uses files from the previous step to perform SVM classification on features extracted from CNN model.
  3. Place all prediction CSV files for which you want to take majority vote in ./results/ and run This will generate majority-voting.csv.

Information about other files

  • 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.
  • Plots.ipynb: IPython notebook used to generate plots present in report.
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