README.md

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The principal task of Sentiment Analysis is to find the perspective ,view ,attitude or feeling of a speaker on a particular topic, event or interactionBasicaly its the analysis of an emotionally cahrged text. Here we try to analyzethe reviewsposted by people at Imdb. Further the reviews are processed analyzed using machine learning procedures, algorithms and other related aspets.

Algorithms Used

* Support Vector Machine Classifier - `linearSvc`
* Random Forest Classifer
* AdaBoost Classfier
* Naive Bayes Classifier - `MultinomialNB`
* Bagging Classifier

Steps in Sentiment Analysis

1.Formation of Dataset
2.Processing of Data
3.Creation of Feature Vector
4.Classification

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Environment Setup

     -:> python 2.8 or above 3.x recommended

Dataset

Download DataSet from here then put aclImdb folder to parent directory

File structure

install modules

1.sklearn

pip install sklearn

2.pickle

pip install pickle-mixin

3.nltk

pip install nltk

in Python IDLE

import nltk
nltk.download("stopwords")

4.numpy

pip install numpy

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imdbReviews.py generates *.pkl files which are the training and testing datasets. First, set the dataset directory in the imdbReviews.py, then run the code.

python imdbReviews.py

now you will get two new .pkl files such as test.pkl & train.pkl which are needed for naive.py, svm.py,rfc.py,bagging.py,adaboost.py.

To do prediction, run the following command.

python filname.py 

eg:-

python naive.py

ScreenShots

Ada Boost Classifier

Bagging Classifier

Naive Bayes - MultinomialNB()

Random Forest Classifier

Support Vector Machine - LinearSVC()