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The aim of this project is to develop a sentiment classifier that can assign a large set of product reviews to the five levels of polarity of opinion as accurately as possible, given a small amount of labeled reviews and a large amount of unlabeled reviews. It is a multi-class classifcation task, where each product review is labeled with one of the five sentiment labels, which are strong negative, weak negative, neutral, weak positive, and strong positive.

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sentiment-classification-for-product-reviews's Introduction

Sentiment-Classification-for-Product-Reviews

Steps to setup and run the "Sentiment-Classification-for-Product-Reviews.ipynb" notebook:

  1. Launch the Jupyter Notebook App.
  2. In the Notebook Dashboard navigate to find the notebook: clicking on its name will open it in a new browser tab.
  3. You can run the notebook document step-by-step (one cell a time) by pressing shift + enter.
  4. You can run the whole notebook in a single step by clicking on the menu Cell -> Run All.

Note:

  1. This code takes approximately 2 hours to run on a windows machine
  2. The additional packages used in this code file must be installed explicitly using "pip" if they are not already installed.

sentiment-classification-for-product-reviews's People

Contributors

suyash0106 avatar

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