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Movie Review Classification

In this project, we’ll learn to classify the text using machine learning approaches. In this project, We’ll be classifying reviews of the IMDB dataset into two different classes i.e. positive or negative. We’ll be learning techniques such as TF-IDF and bag of words. 

What will you Learn in the Project?

  • Cleaning and processing the text data
  • Performing feature extraction
  • Calculate tf-IDF and word of bags for vectorizing data  
  • Build machine learning text classifier models. 


  1. Working knowledge of scikit-learn library
  2. Theoretical understanding of different machine learning classification algorithms
  3. Understanding of TF-IDF and Bag of words approach.

Tools Used

  1. Seaborn and matplolib for plotting the graphs
  2. Nltk for text pre-processing
  3. Sklearn for building machine learning models
  4. Textblob for word-cloud

Tasks to be Performed

We will be performing following tasks as part of this project:

Task-1: Load the data and perform the Exploratory Data Analysis

Task-2: Clean the data by removing HTML strips, noise text & special characters

Task-3: Perform the text stemming from the text data

Task-4: Remove the stopwords from the text data

Task-5: Compute statistical features using tf-IDF and bag of words technique

Task-6: Build the machine learning models for classifying the reviews

Task-7: Evaluate the model on the test and state the best one

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Skills you will develop

Processing the text data

Feature extraction through tf-idf and bag of words

Building machine learning models for text classification

Plotting word cloud for representing the text data

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