Abstract
Due to the immense amounts of texts on the internet and the qualitative nature of human sentiment and the characteristics of machine learning and deep learning algorithms, they are potential candidates to be appli-ed in textual sentiment analysis. To compare the effectiveness of different algorithms, processed data using TF-IDF is input into different algorithms respectively, and the accuracy scores of the trials using the identical data-set are recorded for comparison. It turns out that the Extra Trees classifier and the Random Forest classi-fier performed the best among machine learning algorithms, suggesting the significance for reducing overfitting in this specific task given that the less overfitting-proof Decision Tree has performed worse. LSTM has a better ac-curacy score than CNN, though it is to be noted that the former runs significantly slower than the latter, indicating efficiency to be a potential topic to be considered.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Academic Journal of Computing & Information Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
