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Sentiment analysis on product reviews with identification of most reviewed products from Amazon product reviews dataset consists of 35000 reviews.

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Arjun-Mota/amazon-product-reviews-sentiment-analysis

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amazon-product-reviews-sentiment-analysis

Sentiment analysis of product reviews in positive and negative with Identification of most reviewed products from Amazon products dataset.

  • Out of 37000 reviews, getting most reviewed products with number of reviews for each.

  • Do a sentiment analysis on how many reviews are positive and negative based on ratings given by the user.

  • Sentiment of reviews were not given beforehand in the dataset, so I took ratings in consideration and marked them Positive (>=3) and Negative (<3).

Installation

  • Install Jupyter Notebook and Python 3.7

  • Install Python libraries mentioned in requirements.txt for this project

  • On top of all the requirements mentioned in requirements.txt, follow installation of Huggingface Transformers on your system as sentiment analysis is being done using the transformer.

  • Click here for installation instructions:
    enter image description here https://huggingface.co/transformers/installation.html

Working with Jupyter Notebook

  • Open notebook (.ipynb) from this project with Jupyter Notebook

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Sentiment analysis on product reviews with identification of most reviewed products from Amazon product reviews dataset consists of 35000 reviews.

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