• Aliya Abdullah
• Anushka Bagde
• Aadarsh Kapoor
• Dr. Varsha Shah

With the dawn of shopping websites it is much easier for small businesses and individual consumers to buy goods. These websites also have reviews and ratings of the available products. Many people also like to use blogging websites like twitter to give feedback on products. But with the wide range of products that are now available, the decision-making process has become much more difficult. This is why we propose a vendor selection tool that can be used by small businesses or consumers trying to find the perfect product. It aggregates reviews and ratings from e-shops as well as the sentiment people have expressed towards the products on blogging websites such as Twitter. It uses web scraping technique to extract reviews and ratings of the people given under each product. Python’s beautifulsoup library is used to scrape reviews and store them in a .csv file. Contents of this .csv file can be accessed and displayed as per requirement. It also uses sentiment analysis technique to extract people’s opinions on the microblogging website Twitter. A Support Vector Machine is trained to analyse tweets that have been manually labelled. Then, this SVM is used to classify product sentiment.

Web scraping, Sentiment analysis, Twitter, Amazon, Support Vector Machine.

"Vendor Selection through Review Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.f153-f157, October-2021, Available :http://www.jetir.org/papers/JETIR2110523.pdf

"Vendor Selection through Review Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppf153-f157, October-2021, Available at : http://www.jetir.org/papers/JETIR2110523.pdf