Twitter-Sentiment-Analysis

Contains machine learning methods used for predicting sentiments for tweets. 🎱 🐦 !

How to run this code yourself:

• This repo assumes you have the tools I used installed.

• Visit the [Tools Used](### Tools Used) section

• Clone this repo. For convenience the parsed dataset has been uploaded in a numpy array format

• To parse the raw data yourself if you've made changes to the parse_dataset.py file you will need the raw dataset, source for the raw data is below in this readme.

• Run python parse_dataset.py

• There are some convenient arguments you can pass in, a list of them are included in the doc block for the main method in parse_dataset.py

• Run python *_runner.py in the folder for the machine learning algorithm you wish to run

• e.g. python *_runner.py --display_graphs

• I may or may not have uploaded the trained models into this repo. If there are no saved models/weights the scripts will automatically retrain.

• Please don't be mad if you have to train most models yourself, disk space on a small SSD is hard to come by

• If you really don't want to train the models yourself but are super curious shoot me an email!

• To (re)train the models run python *_runner.py --retrain

• To run cross validation to train the best fine tuned model run python *_runner.py --retrain --cross-validate

• To display the graphs run python *_runner.py --display_graphs

• To see the accuracy results for a model run python *_runner.py --test=test_set

• No I did not cheat while training, commit history will show everything I've done.

• To use all of the data run python *_runner.py --give_me_the_data

• WARNING: this will easily consume 16+ GB of RAM for most of the models

Data Source Used:

• http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip
• ~1.6 million tweet dataset with positive/negative sentiment attached

Machine Learning Methods Used:

• Naive Bayesian Network
• LDA (Latent Dirichlet Allocation)
• TODO: Linear Regression
• TODO: SVM
• TODO: Logistic Regression
• TODO: Neural Networks

Tools Used:

• NumPy
• sklearn
• TensorFlow
• NLTK
• PyBrain
• Install these by running pip install {package name} in your terminal/shell

Inquiries:

Please email me at ramaneek.gill@hotmail.com

Research Paper Proposal

We plan on creating a short text sentiment analyzer. This project will be using a dataset of ~1.6 million tweets (from: http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip) that contains tweet text from twitter along with a boolean flag showing whether the tweet is positive or not.

We will be training two models on this data. One will be trained using a Bayesian Network, the other will be trained using Latent Dirichlet allocation (LDA). The two models' classification errors, precision, and recall will be compared against each other in order to determine if the independence assumed from a Bayesian Network has an advantage or disadvantage over a Gaussian class-conditional density in an LDA model for short texts.

TODO:

Stemming support, this should reduce input noise. Yay for feature engineering!

References

Li, F., Huang, M., & Zhu, X. (2010, July). Sentiment Analysis with Global Topics and Local Dependency. In AAAI (Vol. 10, pp. 1371-1376). https://www.cs.princeton.edu/~blei/papers/Blei2011.pdf

Bai, X. (2011). Predicting consumer sentiments from online text. Decision Support Systems, 50(4), 732-742. http://www.sciencedirect.com/science/article/pii/S016792361000148X

Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1, 12. http://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf