TY - JOUR
T1 - Automatic Construction and Global Optimization of a Multisentiment Lexicon
AU - Yang, Xiaoping
AU - Zhang, Zhongxia
AU - Zhang, Zhongqiu
AU - Mo, Yuting
AU - Li, Lianbei
AU - Yu, Li
AU - Zhu, Peican
N1 - Publisher Copyright:
© 2016 Xiaoping Yang et al.
PY - 2016
Y1 - 2016
N2 - Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent.
AB - Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010). This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset. In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon. Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc. Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks. It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent.
UR - http://www.scopus.com/inward/record.url?scp=85006084802&partnerID=8YFLogxK
U2 - 10.1155/2016/2093406
DO - 10.1155/2016/2093406
M3 - 文章
C2 - 28042290
AN - SCOPUS:85006084802
SN - 1687-5265
VL - 2016
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 2093406
ER -