Sentiment Analysis High_School' Feedback for Predicting Career Possibilities
Nguyen Thi Phuong Giang, Danh Thi Ngoc Anh, Truong Minh Tuan · 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) · 2022
For many years, the issue of admissions counseling has been of interest to many people, especially students and parents. Choosing the right career, following your abilities and skills is extremely necessary. However, the traditional form of admissions counseling makes students feel bored and uninterested. In addition, understanding students' feelings and feedback is an essential issue when directing aspirations to universities or colleges. Knowing this, the study offers a solution to help high school students understand their abilities and abilities to choose the right career and knack that they love. The student feedback dataset has been collected by the author since 2015. Next, manually removing all junk, duplicate, or invalid data, the author has more than 52,000 raw data. The author then used three algorithms, Naïve Bayes, SVM, and Entropy to build a model for analyzing feedback on students' feelings based on classification algorithms. The results proved that the Maximum Entropy algorithm was better than Naïve Bayes and that vectors supported with a score of 78%. With such accuracy, the author found that the data and results of the study could be useful data sources for the community to analyze feedback on future feelings.