Social Media Comment Management using SMOTE and Random Forest Algorithms

DOI

10.2991/ijndc.2018.6.4.2How to use a DOI?

Keywords

Social media; imbalanced dataset; Random Forest; SMOTE

Abstract

Comment posting is one of the main features found in social media. Comment responses enrich the social network function especially for how-to content. In this work, we focus on cooking video clips which are popular among users. Various questions found in comments need to be clustered in order to facilitate the clip owners to effectively provide responses for those viewers. We applied machine learning algorithms to learn and classified comments into predefined classes. Then the density-based clustering algorithm, for density-based spatial clustering of applications with noise, is applied to cluster the content of Comment. The experimental result show that using Random Forest with SMOTE provides the best performance. We got 95% of the average performance measured in term of F₁-measure. Furthermore, we implement the incremental learning system via an online application that can automatically retrieve and organize video clip’s comment into categories.

Copyright

© 2018 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Nuanwan Soonthornphisaj
AU  - Taratep Sira-Aksorn
AU  - Pornchanok Suksankawanich
PY  - 2018
DA  - 2018/09/28
TI  - Social Media Comment Management using SMOTE and Random Forest Algorithms
JO  - International Journal of Networked and Distributed Computing
SP  - 204
EP  - 209
VL  - 6
IS  - 4
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2018.6.4.2
DO  - 10.2991/ijndc.2018.6.4.2
ID  - Soonthornphisaj2018
ER  -