Product rating generation based on public opinion using sentiment analysis
Abstract
In modern days, people tend to check reviews and opinions on a product before buying.
The main goal of our system is according to the public opinion of a product, give a rating to
the product on a scale of 0 to 10. Also, we are plotting this rating for better understanding of
how every attribute of a product stands against time. For public opinion, we collected data from
twitter. The reason behind using twitter tweets is because it is one of the most popular social
sites. The data is pre -processed and then filtered for irrelevant characters. The data is then
clustered based on different attributes of product. After that using the Naïve Bayes classifier
we do a sentiment analysis of the data to calculate the polarity. This polarity is then converted
to a scale of 0 to 10 (where 5 is average) and thus the rating of an individual product is obtained.
This polarity for each product for every attribute is plotted in a graph where axis-x represents
time and axis-y represents polarity.