Automatic Construction and Global Optimization of a Multisentiment Lexicon.

Xiaoping Yang,Peican Zhu,Lianbei Li,Yuting Mo,Li Yu,Zhongqiu Zhang,Zhongxia Zhang

doi:10.1155/2016/2093406

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.

Highlights

Opinion mining and sentiment analysis of online text have become a hot research area in recent years, which includes opinion summarization and sentiment classification
Considering the above points, this paper presents an unsupervised model of automatic construction of a multisentiment lexicon based on WLI neural network language model [21] and a global optimization framework
Evaluation of SentiRuc in Sentiment Analysis Tasks. This experiment investigates the performance of sentiment analysis task using different lexicons. 3,100 sentences are selected from NLPCC 2013 Competition and NLPCC 2014 Competition and 3,700 sentences containing one of the 148 multisentiment words are selected from Sina Microblog

Summary

Introduction

Opinion mining and sentiment analysis of online text have become a hot research area in recent years, which includes opinion summarization and sentiment classification. Most of these tasks would benefit from a high quality sentiment lexicon which could provide excellent sentiment features when no training data is available. The primary form of sentiment lexicons is binary annotation with positive and negative labels, such as Sentiwordnet developed by Italian Information Technology Research Institute [1, 2], the Chinese general sentiment lexicon (NTUSD) [3] annotated by Taiwan University, the Chinese emotion dictionary from the Chinese Academy of Sciences, and English Xsimilarity. Multiple sentiment lexicons with assignments of the strength of sentiments are constructed, such as the Affective Lexicon Ontology of Dalian University of Technology (DUT Ontology) [4]. Few works evaluated and optimized the accuracy of intensity annotation by introducing all possible linguistic heuristics

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