@inproceedings{c7ce86386e54475b998a46550fcbf215,
title = "A Two-Level Classifier Model for Sentiment Analysis",
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.",
keywords = "POS, Predict time, Sentiment analysis, Two-level classifier model, Weaken words",
author = "Haidong Hao and Li Ruan and Limin Xiao and Shubin Su and Feng Yuan and Haitao Wang and Jianbin Liu",
note = "Publisher Copyright: {\textcopyright} 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 13th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2017 ; Conference date: 11-12-2017 Through 13-12-2017",
year = "2018",
doi = "10.1007/978-3-030-00916-8\_64",
language = "英语",
isbn = "9783030009151",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "704--717",
editor = "Imed Romdhani and Lei Shu and Timothy Gordon and Hara Takahiro and Zhangbing Zhou and Deze Zeng",
booktitle = "Collaborative Computing",
address = "德国",
}