Mining Sentiment for Web Short Texts Based on TSCM Model

doi:10.3969/j.issn.0372-2112.2016.08.017

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Mining Sentiment for Web Short Texts Based on TSCM Model

更新时间：2025-07-08

- Mining Sentiment for Web Short Texts Based on TSCM Model
- Acta Electronica Sinica Vol. 44, Issue 8, Pages: 1887-1891(2016)
- 作者机构：
  
  1. 福建师范大学软件学院,福建,福州,350007
  2. 广西师范学院计信学院,广西,南宁,530023
  3. 福建师范大学软件学院,福建,福州,350007
  4. 广西师范学院计信学院,广西,南宁,530023
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61370078, No.61363037);Youth Fund of Humanities and Social Science Research Projects of Ministry of Education of China (No.12YJCZH074);Science and Technology Project of Department of Education of Fujian Province (No.JA13077)
- DOI：10.3969/j.issn.0372-2112.2016.08.017
  CLC： TP273
- Published Online：25 August 2016，
  
  Published：2016
- 稿件说明：
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Mining Sentiment for Web Short Texts Based on TSCM Model[J]. Acta Electronica Sinica, 2016, 44(8): 1887-1891.
DOI：

Mining Sentiment for Web Short Texts Based on TSCM Model[J]. Acta Electronica Sinica, 2016, 44(8): 1887-1891. DOI： 10.3969/j.issn.0372-2112.2016.08.017.

摘要

针对网络短文本情感挖掘问题，提出一种新的基于LDA和互联网短评行为理论的主题情感混合模型TSCM，TSCM模型中的整篇评论中每个句子的主题分布是不同的，TSCM产生词的流程是先确定词的情感极性，再确定词的主题，TSCM考虑了词与词之间的联系.真实数据集Movie与Amazon上的大量实验表明，与代表性算法JST、S-LDA、D-PLDA和SAS相比较，TSCM模型能对用户真实情感与讨论主题进行更加有效的分析建模.

Abstract

For sentiment analysis of web short texts

a topic sentiment combining model (TSCM) is proposed based on LDA and web review behavioral theory

which is founded on the assumption that topic distribution of each sentence in a review is unique and different from that of other sentences.Generative process of TSCM is to first determine sentiment orientation of each word and then topic of each sentence in a review while taking word relation into consideration.Extensive experiments on real-world datasets (Movie and Amazon) show that TSCM significantly outperforms JST

S-LDA

D-PLDA and SAS in terms of the accuracy of sentiment classification and topic detection.

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