Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2018

Abstract

Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good performance for analyzing small datasets, while for large datasets, ELM performs better than SVM. This research also indicates that ELM has the potential application in the domain of social media analysis.

Keywords

ELM, SVM, Sentiment Classification, Social Media, Learningbased Method

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

Proceedings of the 9th International Conference on Extreme Learning Machine (ELM2018), Singapore, November 21-23

Volume

11

First Page

336

Last Page

344

Identifier

10.1007/978-3-030-23307-5_36

Publisher

Springer

City or Country

Singapore

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-030-23307-5_36

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