poojan-dalal/amazon_reviews_analyzer

amazon_products_issue_analyzer

VIDEO/IMAGES LINK: https://drive.google.com/drive/folders/16At-dYrGpqYliNBEYDTJJrbLF44C9t2a?usp=sharing

E-commerce has revolutionized the way we shop. Thousands of reviews get uploaded online on a daily basis and it's always a task to fetch if a product/service is actually good or not

Problem statement:

One of the biggest challenges is verifying the authenticity of the product

Thousands of companies sell their products online and each product receives hundreds of comments that include positive points led by the customers as well as negative points that needs to be improved by the company in order to take a lead in market competition

Manually reading and finding product issues and complains is tedious task and can be automated using NLP and AI

Why is review analysis important?

The majority of buyers read online reviews before making new purchases, considering them to be informative and accurate. It has been observed that negative reviews had a particularly dramatic influence on people's buying behaviours.

Monitoring and understanding consumer reviews gives you priceless insight into customer experience, attitudes, and enables you to stay in tuned with market shifts and changes.

Customer reviews on Amazon provide businesses a lot of information – what they liked or didn't like about the product, what they are missing in the current market, and even the level of interest in future products. This information will also enable you to identify customer pain point and deal with them before launching a new product.

Solution:

Amazon analyzer provides accurate and relevant data for analysis.

This system will serve two purposes:

1. Enable consumers to quickly extract the key topics covered by the reviews without having to go through all of them
2. Help the sellers/retailers get consumer feedback in the form of topics

Amazon analyzer algorithms scan and analyze the data, identifying customer sentiment and flagging pain points. The end result is highly granular Amazon review analysis for data-driven decision making that will elevate your brand to a whole new level.

Working

The directory Back-End contains the required python flask, scraping and NLP codes. The directory Front-End is built React

Technology used:

It used technology such as react, NLP: NLTK, flask rest API, firebase real-time Database

(Front end) It is easy to use where companies paste their product link

(Back End) all the comments of the product will be scrapped and analysed using complex NLP methods

(Response: Front-End) Data visualisation of major plus points of the product, major complains, pie charts depicting issues faced, timeline of complains and improvement of product etc