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https://github.com/abdulfatir/twitter-sentiment-analysis

Sentiment analysis on tweets using Naive Bayes, SVM, CNN, LSTM, etc.
https://github.com/abdulfatir/twitter-sentiment-analysis

cnn deeplearning keras lstm machine-learning python sentiment-analysis sentiment-classification

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

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README 

        
# Sentiment Analysis on Tweets



![Status badge](https://img.shields.io/badge/Status-Archived-important)



**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](https://github.com/abdulfatir/twitter-sentiment-analysis/tree/master/docs/report.pdf) 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.  



## Requirements



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.



## Usage



### Preprocessing 



1. Run `preprocess.py ` on both train and test data. This will generate a preprocessed version of the dataset.

2. Run `stats.py ` where `` 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: ``, ``, ``, and `` 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.



### Baseline

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



### Naive Bayes

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



### Maximum Entropy

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.



### Decision Tree

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



### Random Forest

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



### XGBoost

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



### SVM

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



### Multi-Layer Perceptron

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



### Reccurent Neural Networks

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`).



### Convolutional Neural Networks

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`). 



### Majority Vote Ensemble

13. To extract penultimate layer features for the training dataset, run `extract-cnn-feats.py `. 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.

15. Place all prediction CSV files for which you want to take majority vote in `./results/` and run `majority-voting.py`. 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.