├── images └── movie-sentiment-3.png ├── pdf ├── Cornell Moview Review Dataset - Sentiment Analysis.pdf └── Stanford Large Movie Review Dataset - Sentiment Analysis.pdf ├── .gitignore ├── README.md ├── data ├── cornell │ └── y_train.txt └── stanford │ └── y_train.txt └── LICENSE /images/movie-sentiment-3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kbastani/sentiment-analysis-movie-reviews/HEAD/images/movie-sentiment-3.png -------------------------------------------------------------------------------- /pdf/Cornell Moview Review Dataset - Sentiment Analysis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kbastani/sentiment-analysis-movie-reviews/HEAD/pdf/Cornell Moview Review Dataset - Sentiment Analysis.pdf -------------------------------------------------------------------------------- /pdf/Stanford Large Movie Review Dataset - Sentiment Analysis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kbastani/sentiment-analysis-movie-reviews/HEAD/pdf/Stanford Large Movie Review Dataset - Sentiment Analysis.pdf -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | lib/ 17 | lib64/ 18 | parts/ 19 | sdist/ 20 | var/ 21 | *.egg-info/ 22 | .installed.cfg 23 | *.egg 24 | 25 | # PyInstaller 26 | # Usually these files are written by a python script from a template 27 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 28 | *.manifest 29 | *.spec 30 | 31 | # Installer logs 32 | pip-log.txt 33 | pip-delete-this-directory.txt 34 | 35 | # Unit test / coverage reports 36 | htmlcov/ 37 | .tox/ 38 | .coverage 39 | .cache 40 | nosetests.xml 41 | coverage.xml 42 | 43 | # Translations 44 | *.mo 45 | *.pot 46 | 47 | # Django stuff: 48 | *.log 49 | 50 | # Sphinx documentation 51 | docs/_build/ 52 | 53 | # PyBuilder 54 | target/ 55 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Movie Review Sentiment Analysis Benchmark 2 | ================================ 3 | 4 | An iPython notebook that tests [Graphify's](http://github.com/graphify/graphify) feature extraction and selection algorithm as a logistic regression classifier. This classifier is benchmarked against [Stanford's Large Movie Review Dataset](http://ai.stanford.edu/~amaas//data/sentiment/) and [Cornell Movie Review Dataset](http://www.cs.cornell.edu/people/pabo/movie-review-data/). 5 | 6 | Content 7 | ------- 8 | 9 | - The slides are at [https://github.com/kbastani/sentiment-analysis-movie-reviews/tree/master/pdf](https://github.com/kbastani/sentiment-analysis-movie-reviews/tree/master/pdf) 10 | 11 | - The interactive notebooks are in the main folder. 12 | 13 | Classification Accuracy 14 | -------- 15 | - Scores `~90%` accuracy on Cornell Movie Review dataset. 16 | - http://nbviewer.ipython.org/github/kbastani/sentiment-analysis-movie-reviews/blob/master/Cornell%20Moview%20Review%20Dataset%20-%20Sentiment%20Analysis.ipynb 17 | - Scores `~80%` accuracy on Stanford Large Movie Review dataset. 18 | - http://nbviewer.ipython.org/github/kbastani/sentiment-analysis-movie-reviews/blob/master/Stanford%20Large%20Movie%20Review%20Dataset%20-%20Sentiment%20Analysis.ipynb 19 | 20 | ###Feature learning 21 | - Features are extracted and learned using Java and Neo4j, and evaluated by building a logistic regression classifier on a weighted tf-idf feature vector. 22 | 23 | Viewing the notebooks online 24 | ---------------------------- 25 | The content of the notebooks can be viewed online through nbviewer.ipython.org. 26 | 27 | Installing Python 28 | ----------------- 29 | For a true interactive use of the notebooks you need to install Python, IPython (for notebooks) and the required libraries scikit-learn, matplotlib and numpy. 30 | 31 | Windows 32 | ------- 33 | You can install everything at once using a complete scientific Python distribution. 34 | Two good ones are the [Enthought Python distribution](http://www.enthought.com/products/epd.php) (EPD, free for academic use) or [Python-(x, y)](http://code.google.com/p/pythonxy/) (free for everyone). 35 | 36 | Mac 37 | --- 38 | For OS X, you can also use the [Enthought Python distribution](http://www.enthought.com/products/epd.php) or the [scipy-superpack](http://fonnesbeck.github.com/ScipySuperpack/). 39 | 40 | 41 | Linux 42 | ----- 43 | Just use your package manager, for example on ubuntu or debian, use 44 | ``apt-get install python ipython python-matplotlib python-numpy python-sklearn``. 45 | 46 | Version requirements 47 | -------------------- 48 | You need to make sure to have at least IPython >= 0.11 installed. You can update using the programm ``easy_install``. 49 | 50 | Installing Scikit-learn 51 | ----------------------- 52 | More tips on installing scikit-learn can be found on the [scikit-learn website](http://scikit-learn.sourceforge.net/dev/install.html#installing-an-official-release). 53 | 54 | 55 | More Resources 56 | -------------- 57 | This repository was modeled off of [tutorial_ml_gkbionics](https://github.com/temporaer/tutorial_ml_gkbionics). 58 | -------------------------------------------------------------------------------- /data/cornell/y_train.txt: -------------------------------------------------------------------------------- 1 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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