Chinese sentiment classification of emotional ontology and word vector based on rules

ZHENG Cheng; CHAO Xu; ZHANG Jin-ping

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Chinese sentiment classification of emotional ontology and word vector based on rules

更新时间：2026-09-04

- Chinese sentiment classification of emotional ontology and word vector based on rules
- Vol. 36, Issue 6, Pages: 50-54(2019)
- 作者机构：
  
  1. 安徽大学计算智能与信号处理实验室,安徽,合肥,230601
  2. 安徽大学计算机学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
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郑诚, 潮旭, 章金平. 基于规则的情感本体和词向量的中文情感分类[J]. 微电子学与计算机, 2019, 36(6): 50-54.

ZHENG Cheng, CHAO Xu, ZHANG Jin-ping. Chinese sentiment classification of emotional ontology and word vector based on rules[J]. 2019, 36(6): 50-54.
郑诚, 潮旭, 章金平. 基于规则的情感本体和词向量的中文情感分类[J]. 微电子学与计算机, 2019, 36(6): 50-54. DOI：

ZHENG Cheng, CHAO Xu, ZHANG Jin-ping. Chinese sentiment classification of emotional ontology and word vector based on rules[J]. 2019, 36(6): 50-54. DOI：

摘要

随着电子商务的快速发展

评论文本的情感倾向研究引起了广大学者的关注.为了充分利用短文本中的情感本体和语义信息

提出结合句法规则、情感本体和词向量的中文情感分类方法.首先利用Word2vec训练词向量

结合句法规则生成短文本向量; 再根据情感特征分布

创建领域自适应情感词典

结合句法规则

得到短文本情感值

从而构建词向量和情感值相结合的情感模型VWEO(Vector with Emotional Ontology).在酒店评论数据集中

与已有方法相比

所提方法在准确率、召回率、F1值均有明显提升.

Abstract

With the rapid development of e-commerce

the research on the emotional tendency of comment texts has attracted the attention of scholars. In order to make full use of the emotional ontology and semantic information in short texts

a Chinese sentiment classification method combining syntactic rules

emotional ontology and word vector is proposed. Word2vec is firstly used to train word vectors and generate short text vectors in conjunction with syntactic rules; Then

according to the distribution of emotional features

a domain adaptive sentiment dictionary is created

and syntactic rules are combined to obtain short text sentiment values

thereby constructing an emotional model called VWEO(Vector with Emotional Ontology) combining word vectors and emotional values. In the hotel review data set

compared with the existing methods

the proposed method has significantly improved accuracy

recall rate and F1 value.

关键词

Keywords

references

XUE B, FU C, ZHAN S. A Study on Sentiment Computing and Classification of Sina Weibo with Word2vec[C]// IEEE International Congress on Big Data.Washington, USA, IEEE, 2014: 358-363.

LIU K L, LI W J, GUO M. Emoticon Smoothed Language Models for Twitter Sentiment Analysis[C]// AAAI Conference on Artificial Intelligence.Toronto, Canada, 2012.

ALMATARNEH S, GAMALLO P. Automatic Construction of Domain-Specific Sentiment Lexicons for Polarity Classification[C]// International Conference on Practical Applications of Agents and Multi-Agent Systems.Avila, Spain, 2018.

姜杰, 夏睿.机器学习与语义规则融合的微博情感分类方法[C]//自然语言处理与中文计算会议.Kunming, China, 2016.

ZHANG D, XU H, SU Z, et al. Chinese comments sentiment classification based on word2vec and SVM perf[J]. Expert Systems with Applications, 2015, 42(4):1857-1863. DOI: 1857

TURNEY P. Thumbs Up or Thumbs Down Semantic Orientation Applied to Unsupervised Classification of Reviews[C]. Proceedings of Annual Meeting of the Association for Computational Linguistics.Philadelphia, USA, 2002: 417-424.

MIKOLOV T, CHEN K, CORRADO G, et al. Efficient Estimation of Word Representations in Vector Space[J]. Computer Science, 2013, 5(4):243-254. DOI: 243

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