Movie Review Analysis

An analysis of the movie_review data set included in the nltk corpus. I would probably add some buzz words here later on.

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Index:

• What is in this repo
  • Accuracy achieved
• Requirements
  • Downloading the dataset
• Running it
• So
• Legal stuff

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What is in this repo

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• An implementation of nltk.NaiveBayesClassifier trained against 5000 movie reviews. Implemented in nltkNB.ipynb
• Using sklearn
  • Naive Bayes:
    • MultinomialNB:
    • BernoulliNB:
  • Linear Model
    • LogisticRegression:
    • SGDClassifier:
  • SVM
    • SVC:
    • LinearSVC:
    • NuSVC:

Implemented in scikitlearnNB.ipynb

• Implemented a voting system to choose the best out of all the learning methods. Implemented in voting_process.ipynb

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Accuracy achieved

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Classifiers	Accuracy achieved
nltk.NaiveBayesClassifier	73.0%
ScikitLearn Implementations
BernoulliNB	72.0%
MultinomialNB	76.0%
LogisticRegression	74.0%
SGDClassifier	69.0%
SVC	48.0%
LinearSVC	74.0%
NuSVC	74.0%

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Requirements

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The simplest way(and the suggested way) would be to install the required packages and the dependencies by using either anaconda or miniconda

After that you can do

$ conda update conda
$ conda install scikit-learn nltk

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Downloading the dataset

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The dataset used in this package is bundled along with the nltk package.

Run your python interpreter

>>> import nltk
>>> nltk.download('stopwords')
>>> nltk.download('movie_reviews') 

NOTE: You can check system specific installation instructions from the official nltk website

Check if everything is good till now by running your interpreter again and importing these

>>> import nltk
>>> from nltk.corpus import stopwords, movie_reviews
>>> import sklearn
>>> 

If these imports work for you. Then you are good to go!

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Running it

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1. Clone the repo

$ git clone https://github.com/prodicus/movieReviewsAnalysis
$ cd movieReviewsAnalysis
## run the ipython server
$ ipython notebook

2. Order of running

3. nltkNB.ipynb

4. scikitlearnNB.ipynb

5. voting_process.ipynb

6. Hack away!

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So

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"So what, Well this is pretty basic!"

Yes, it is but hey we all do start somewhere right?

Psst. I am working on a spam filtering system. You know the one in which you paste an email and then it tells you whether it is a spam or not.

You can follow me on twitter @tasdikrahman to keep tabs on it.

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Legal stuff

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Hacked together by Tasdik Rahman under the MIT License

You can find a copy of the License at http://prodicus.mit-license.org/