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Repository Details
I performed sentiment analysis on customer feedback reviews for a restaurant. The customers had rated ambiance, food, service, experience and hygiene, and described their experience and provided suggestions. I combined the experience and suggestion as a single field called ‘Review’. Next, I used the Python library, TextBlob, to label each Review as positive, negative or neutral. After this, I created two word clouds using the WordCloud library; one to show the words that were frequently appearing in positive reviews and the other one to show the words frequently occurring the in the negative reviews. This would give owners an idea about what is causing negative and positive feedback. I visualize the results of the analysis where I should the average ratings for each category mentioned above, along with names of server who served the customers that gave negative reviews, so that the cause of the negative experience could be determined and improved.
