Sentiment Classification for Consumer Word-of-Mouth in Chinese: Comparison between Supervised and Unsupervised Approaches
- DOI
- 10.2991/icebi.2010.56How to use a DOI?
- Keywords
- Sentiment classification, Supervised approach, Semantic orientation approach, Chinese
- Abstract
Sentiment classification aims at mining word-of-mouth, reviews of consumers, for a product or service by automatically classifying reviews as positive or negative. Few empirical studies have been conducted in comparing the different effects between machine learning and semantic orientation approaches on Chinese sentiment classification. This paper adopts three supervised learning approaches and a web-based semantic orientation approach, PMI-IR, to Chinese reviews. The results show that SVM outperforms naive bayes and N-gram model on various sizes of training examples, but does not obviously exceeds the semantic orientation approach when the number of training examples is smaller than 300.
- Copyright
- © 2010, 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/).
Cite this article
TY - CONF AU - Ziqing Zhang AU - Qiang Ye AU - Wenying Zheng AU - Yijun Li PY - 2010/12 DA - 2010/12 TI - Sentiment Classification for Consumer Word-of-Mouth in Chinese: Comparison between Supervised and Unsupervised Approaches BT - Proceedings of the 1st International Conference on E-Business Intelligence (ICEBI 2010) PB - Atlantis Press SP - 405 EP - 411 SN - 1951-6851 UR - https://doi.org/10.2991/icebi.2010.56 DO - 10.2991/icebi.2010.56 ID - Zhang2010/12 ER -