Research Article
A Two-Level Classifier Model for Sentiment Analysis
@INPROCEEDINGS{10.1007/978-3-030-00916-8_64, author={Haidong Hao and Li Ruan and Limin Xiao and Shubin Su and Feng Yuan and Haitao Wang and Jianbin Liu}, title={A Two-Level Classifier Model for Sentiment Analysis}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings}, proceedings_a={COLLABORATECOM}, year={2018}, month={9}, keywords={Sentiment analysis POS Weaken words Two-level classifier model Predict time}, doi={10.1007/978-3-030-00916-8_64} }- Haidong Hao
Li Ruan
Limin Xiao
Shubin Su
Feng Yuan
Haitao Wang
Jianbin Liu
Year: 2018
A Two-Level Classifier Model for Sentiment Analysis
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-00916-8_64
Abstract
This paper proposes a fast and high performance classifier model for sentiment analysis of textual reviews. The key contribution is three fold. First, a two-level classifier model consists of three base classifiers is proposed, and theory proves that the model could be better than the strongest classifier among the base classifiers in both classification performance and time cost of predict. Second, this paper proposes a lexicon-based classifier as a base classifier using a new part of speech (POS) which is called “weaken words”. Finally, we implemented several two-level classifiers by combining the lexicon-based classifier with several machine learning classifiers. Experiments on Chinese reviews dataset show that the two-level classifier model is effective and efficient.