To read this content please select one of the options below:

Characters-based sentiment identification method for short and informal Chinese text

Qiujun Lan (Business School, Hunan University, Changsha, China)
Haojie Ma (Business School, Hunan University, Changsha, China)
Gang Li (School of Information Technology, Deakin University, Melbourne, Australia)

Information Discovery and Delivery

ISSN: 2398-6247

Article publication date: 19 February 2018




Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing with a lot of informal expressions, which lead to high computational complexity.


A method based on Chinese characters instead of words is proposed. This method represents the text into a fixed length vector and introduces the chi-square statistic to measure the categorical sentiment score of a Chinese character. Based on these, the sentiment identification could be accomplished through four main steps.


Experiments on corpus with various themes indicate that the performance of proposed method is a little bit worse than existing Chinese words-based methods on most texts, but with improved performance on short and informal texts. Especially, the computation complexity of the proposed method is far better than words-based methods.


The proposed method exploits the property of Chinese characters being a linguistic unit with semantic information. Contrasting to word-based methods, the computational efficiency of this method is significantly improved at slight loss of accuracy. It is more sententious and cuts off the problems resulted from preparing predefined dictionaries and various data preprocessing.



The Research Sponsored by Natural Science Foundation of China (Grant No. 71171076) and the key project of National Natural Science Fund of China (Grant No. 71431008).


Lan, Q., Ma, H. and Li, G. (2018), "Characters-based sentiment identification method for short and informal Chinese text", Information Discovery and Delivery, Vol. 46 No. 1, pp. 57-66.



Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles