]]> important role. Based on the feedback we can decide whether the desired item is good or not, get a
picture of the seller and so on. Many companies that have online shops display the most positive
feedback while hiding bad ones or display only a few of them. In this research, we will help people
by automating the process of deciding whether a feedback is positive or negative, which will give
them time for other jobs and save money for hiring people who will work on the feedback. Since
feedback on online articles is very important today, the process of determining positive and
negative feedback should be made as quick and easy as possible. In this research, we will show a
very simple and fast way to classify feedback as positive or negative, which means that the main
question of this research is how to facilitate and speed up the process of determining the polarity of
the feedback. We will use NLP using Python’s library called TextBlob. The used algorithm is called
Naïve Bayes, it gave the accuracy of around 80%.
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