Sentiment Analysis of Social Media Comments based on Random Forest and Support Vector Machine
Shuo Wen, Peixin Wang, Jinying Zheng, Weiting Huang, Zhi‐Long Ye, Xiaomin Li, Lixuan Zhao · 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · 2022
The rapid advancement of social media has enabled people from all around the world to create, express and share their feelings easily and freely. It also generates voluminous data in various fields including health, education, finance and security etc. The huge amount of data is beneficial for research and analysis. In this study, a model based on algorithm of Random Forest and SVM is developed to classify the sentiment in social media comments. Nearly 100 thousand posts data on Chinese Language are taken from social media comments, then trained and tested using emotion theories as well as machine learning techniques. Each post would be rated based on the sentiment behind it on a scale of positive and negative. The model is later used to analyze specific incident to strengthen its accuracy, then cluster valuable comments. The study would facilitate the decision-making process and various measures can be taken to enhance the management of government.