aalind0 / movie_reviews-sentiment_analysis Goto Github PK

Python 3.5 classification of movie reviews (positive or negative) using NLTK-3 and sklearn.

An analysis of the movie_review data set included in the nltk corpus.

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

Implemented in Scikit_Learn_Classifiers.py

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

The simplest way(and the suggested way) would be to install the 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

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!

1. Clone the repo

$ git clone https://github.com/aalind0/Movie_Reviews-Sentiment_Analysis
$ cd Movie_Reviews-Sentiment_Analysis

2. Order of running

3. NLTK_Naive_Bayes.py

4. Scikit_Learn_Classifiers.py

5. Voting_Algos.py

6. Hack away!

"So what, Well this is pretty basic!"

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

Coming Up. I am working on a Twitter Sentiment Analysis project which first trains on a given data-set and then takes in the live twitter feeds, analyses them plus plots them for data visualization.

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

Hacked together by Aalind Singh.