Sentimental Analysis One of the hot topics in data science is sentimental analysis. The link below provides you a tutorial on learning fundamentals in sentimental analysis. Read the article in the link by yourself and finish a project for sentimental analysis. s:l/monk com/sentiment-analvsis When you demo your project, you should prepare some reviews for processing. Training set: at least twenty reviews including both negative and positive reviews. Then classify another 100 reviews using two of the three algorithms below: o rule-based algorithm o Naive Bayes o Linear Regression Compare your results to the real categories of the tested 100 reviews. Check which algorithm provides more accurate result. . Note: 1) You can create two files to store the reviews. (e.g. training.csv and test.csv) Data set you can use 2) If you use more advanced algorithm (e.g. SVM, Deep Learning) to process a large data set, you would get more points. 3) If you implement sentimental analysis using Python scikit-learn and show the comparison of the results (e.g. execution time, accuracy) between your Python program and C program, you would get more points 3) You can use processes in your project to make your program run faster. 4) If you are unclear about the subject of this topic, please feel free to contact your instructor Sentimental Analysis One of the hot topics in data science is sentimental analysis. The link below provides you a tutorial on learning fundamentals in sentimental analysis. Read the article in the link by yourself and finish a project for sentimental analysis. s:l/monk com/sentiment-analvsis When you demo your project, you should prepare some reviews for processing. Training set: at least twenty reviews including both negative and positive reviews. Then classify another 100 reviews using two of the three algorithms below: o rule-based algorithm o Naive Bayes o Linear Regression Compare your results to the real categories of the tested 100 reviews. Check which algorithm provides more accurate result. . Note: 1) You can create two files to store the reviews. (e.g. training.csv and test.csv) Data set you can use 2) If you use more advanced algorithm (e.g. SVM, Deep Learning) to process a large data set, you would get more points. 3) If you implement sentimental analysis using Python scikit-learn and show the comparison of the results (e.g. execution time, accuracy) between your Python program and C program, you would get more points 3) You can use processes in your project to make your program run faster. 4) If you are unclear about the subject of this topic, please feel free to contact your instructor