Sentiment Analysis is really important for understanding what people think and feel when they write something. This is a part of Natural Language Processing. The old way of doing Sentiment Analysis just looks at if something is good, bad or neutral. It does not try to figure out why people feel that way. This system uses Machine Learning and Deep Learning to find out how people feel and why they feel that way. It looks at the words people use and tries to understand what they mean. The system has a parts: it gets the text ready it finds the important words it uses special models like LSTM and CNN to see if something is good or bad and it tries to find the reasons why people feel that way. The new system is really good at finding out how people feel and why. It is better than the way of doing things because it can explain why it thinks something is good or bad. We used ways to test the system, such as looking at how often it is right how precise it is and how well it can find the good and bad things. We also used charts, like the Confusion Matrix and ROC-AUC to see how well the system works. We used charts to see how well the system works. The Confusion Matrix and ROC-AUC were two charts we looked at. We looked at the Confusion Matrix and the ROC-AUC because we wanted to know how well the system works. The Confusion Matrix and ROC-AUC helped us understand the system. The Confusion Matrix and the ROC-AUC really helped us understand the system.
Sentiment Analysis; Machine Learning; Natural Language Processing (NLP); Social Media Analytics
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