The multilayer sentiment analysis model based on Random forest

DOI

10.2991/ameii-16.2016.246How to use a DOI?

Keywords

text sentiment analysis, multi-features multi-base-classifiers meta ensemble learning sentiment analysis model, machine learning, situational awareness

Abstract

With the rapid development of the Internet, artificial intelligence has gain widespread concern. Under the background, as one closely related discipline sentiment analysis's relevant research work have also been expanded. First, the paper analy existing text sentiment analysis method, compare the effect of a variety of emotional classification trained by traditional machine learning model. Second, it introduce ensemble learning methods, use random forest as meta learning method train base classifiers which trained through different feature sets. Though the experiments concluded that: by using a different set of features and different base classifiers, the ensemble model can obtain significant promotion, so the paper propose a new model " MFMB-ME,Multi-Features Multi-Base-Classifiers Meta Ensemble Learning Sentiment Analysis Model".

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Wei Liu
AU  - Jie Zhang
PY  - 2016/04
DA  - 2016/04
TI  - The multilayer sentiment analysis model based on Random forest
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.246
DO  - 10.2991/ameii-16.2016.246
ID  - Liu2016/04
ER  -