Many local small business owners run out of business because of lack of efficient way of analyzing their overall customer feed till date.In order to secure their investments and understand the overall customer satisfaction we came up with the idea of analyzing the various user reviews and provide a comprehensive reporting application which will help them to take significant decisions.
What it does
It considers reviews from various customers and builds a visualized model by highlighting the customer sentiments
How we built it
We used machine learning supervised models like Decision tree,Naive Bayes to develop and classify the various customer reviews.
Challenges we ran into
Finding training and test datasets for training the model,Compatibility between data and model used for classification,finding out the best model for sentiment analysis,
Accomplishments that we're proud of
We were able to develop a prototype that generates visual data based on customer reviews and categorize them into sentiments
What we learned
Teamwork,Hard-work,Good planning,Analysis,Better debugging skills
What's next for Sentiment Analysis Of Restaurants Data
Providing better suggestion to customers based on sub categories like ambiance, authentic , hygiene Improve the depth of categorization from level 2 to level 5(excellent,good,average,poor,terrible,)