By Parth Mistry

• Contains 50K movie reviews for natural language processing or Text analytics.
• This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.
• We have a set of 25,000 highly polar movie reviews for training and 25,000 for testing.
• So, predict the number of positive and negative reviews using either classification or deep learning algorithms.
• Here we will be using Logistic Regression to classify the reviews.

Python Version: 3.7
Packages: pandas, numpy, sklearn, nltk, pickle
Dataset: IMDB Movie Reviews

1. Transforming Documents to Feature Vectors
2. Checking word relevancy using TF-IDF
3. Calculating TF-IDF of each term
4. Removing noisy data
5. Tokenization of documents
6. Transforming Text Data into TF-IDF Vectors
7. Document Classsification using Logistic regression

LogisticRegressionCV(cv=5,
                    scoring='accuracy',
                    random_state=0,
                    n_jobs=-1,
                    verbose=3,
                    max_iter=300)

• Here, I used Logistic Regression on the cleaned data, and it was trained with 89% of accuracy classifying movie reviews.

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