一种融合词序信息的多粒度文本话题情感联合模型

赵煜; 邵必林; 边根庆

doi:10.7652/xjtuxb201411018

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一种融合词序信息的多粒度文本话题情感联合模型

更新时间：2025-07-09

- 一种融合词序信息的多粒度文本话题情感联合模型
- A Joint Model for Multi-Granularity Topics and Sentiments with Fusing Word Order Information
- 西安交通大学学报 2014年48卷第11期页码：103-108
- 作者机构：
  
  西安建筑科技大学管理学院,西安,710055
- 作者简介：
- 基金信息：
  
  : 2014-03-26。作者简介: 赵煜(1981—),男,博士生;邵必林(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61272458)
- DOI：10.7652/xjtuxb201411018
  中图分类号： TP391
- 网络首发：2014-11-10，
  
  纸质出版：2014
- 稿件说明：
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赵煜, 邵必林, 边根庆. 一种融合词序信息的多粒度文本话题情感联合模型[J]. 西安交通大学学报, 2014,48(11):103-108.

A Joint Model for Multi-Granularity Topics and Sentiments with Fusing Word Order Information[J]. 2014, 48(11): 103-108.
赵煜, 邵必林, 边根庆. 一种融合词序信息的多粒度文本话题情感联合模型[J]. 西安交通大学学报, 2014,48(11):103-108. DOI： 10.7652/xjtuxb201411018.

A Joint Model for Multi-Granularity Topics and Sentiments with Fusing Word Order Information[J]. 2014, 48(11): 103-108. DOI： 10.7652/xjtuxb201411018.

摘要

针对基本话题模型只能抽取粗粒度上下文信息的问题

通过对潜在狄里克雷分配(LDA)模型进行扩展

建立了一种利用词序信息的多粒度话题情感联合模型(MTSU-Col)。MTSU-Col模型客观表达了词汇、全局/局部话题、情感标签和词序信息之间的关联关系

使模型中话题和情感的建模更加符合文本的语义表达

有效解决了现有话题、情感分析方法存在的领域依赖问题

从而实现了文本多粒度话题信息和情感倾向信息的同步非监督获取。实验表明:利用MTSU-Col模型对文本进行情感倾向性分类

可使综合评价指标F

值达到84%

整体性能与监督分类方法支持向量机(SVM)类似

均优于未采用词序信息的分析方法。由于挖掘话题集合具有层次化、语义相关的特点

因此MTSU-Col模型对观点挖掘是可行、有效的。

Abstract

A joint model for multi-granularity topics and sentiments(MTSU-Col model)based on an extension to LDA model by incorporating collocation is proposed to solve the problem that the basic topic model captures only coarse-granularity contextual information. The MTSU-Col model objectively expresses the correlative relationship among words

global\local topics

sentiment labels and collocation

allows us to infer topics and sentiment information

and provides a closer match to real semantic representation contained in texts. The MTSU-Col model synchronously realizes an unsupervised mining of multi-granularity topics and sentiment information

and effectively solves the domain dependent problem in existing methods. Experimental results show that the proposed

model achieves F

of 84% for sentiment classification

and its performance is comparable to the performance of SVM methods. Since the mining collection of topics is hierarchy and semantic related

it is feasible and effective to use the proposed model for opinion mining.

关键词

Keywords

references

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