Automatic Construction and Global Optimization of a Multisentiment Lexicon.

Abstract:

: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.

journal_name

Comput Intell Neurosci

authors

Yang X,Zhang Z,Zhang Z,Mo Y,Li L,Yu L,Zhu P

doi

10.1155/2016/2093406

subject

Has Abstract

pub_date

2016-01-01 00:00:00

pages

2093406

eissn

1687-5265

issn

1687-5273

journal_volume

2016

pub_type

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