Managed to classify twitter hashtag data into positive and negative reviews. From the first use of UiPath to the project maintenance stage, we have followed the Waterfall model for the same. As soon we knew about the requirement we started designing the flow of sequences and charts for the project. Our python development team started scripting the Natual Language Understanding (NLP and NLU) logic to get data from the Excel sheet we scraped from Twitter. Once everything was on track, we implemented the project on the studio. Several tests were run on the basis of Black Box Testing. Our nontechnical staff also used the project and it went smoothly. Finally, we rolled the project to our partner firm. We are still maintaining the project for them.
I would probably like to do this for facebook. Since it was successful for twitter we are planning to increase the data storage capability so that we can store the twitter data and Facebook data same time. Multiple UiPath Robots can do same work in no time. We can help our customers more by centralizing the reviews from Social media giants.