Biterm topic model of social network users’ sentiment by integrating word co-occurrence

Qiuyang GU; Bao WU; Chunhua JU

doi:10.11959/j.issn.1000-0801.2020302

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Biterm topic model of social network users’ sentiment by integrating word co-occurrence

Research and Development | 更新时间：2024-06-05

- Biterm topic model of social network users’ sentiment by integrating word co-occurrence
- Telecommunications Science Vol. 36, Issue 11, Pages: 47-60(2020)
- 作者机构：
  
  1. 浙江工业大学管理学院，浙江杭州 310023
  2. 浙江工业大学中国中小企业研究院，浙江杭州 310023
  3. 浙江工商大学管理工程与电子商务学院，浙江杭州 310018
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020302
  CLC： TP18
- Published Online：2020-11，
  
  Published：20 November 2020
- 稿件说明：
移动端阅览
Qiuyang GU, Bao WU, Chunhua JU. Biterm topic model of social network users’ sentiment by integrating word co-occurrence[J]. Telecommunications science, 2020, 36(11): 47-60.
DOI：

Qiuyang GU, Bao WU, Chunhua JU. Biterm topic model of social network users’ sentiment by integrating word co-occurrence[J]. Telecommunications science, 2020, 36(11): 47-60. DOI： 10.11959/j.issn.1000-0801.2020302.

摘要

近年社交网络用户数量不断增加，基于文本的用户情感分析技术得到普遍关注和应用。但数据稀疏性、精度较低等问题往往会降低情感识别方法的精度和速度，提出了用户情感Biterm主题模型（US-BTM），从特定场所的文本中发现用户偏好及情感倾向，有效利用Biterm进行主题建模，并使用聚合策略形成伪文档，为整个文本集创建词汇配对以解决数据稀疏性和短文本等问题。通过词汇共现算法对主题进行研究，推断文本集级别信息的主题，并通过分析特定场景下的评论文本集中的词汇配对集及其相应主题的情感，达到准确预测用户对特定场景的兴趣、偏好和情感的目的。结果证明，所提方法能准确地捕捉用户的情感倾向，正确地揭示用户偏好，可广泛应用于社交网络的内容描述、推荐及社交网络用户兴趣描述、语义分析等多个领域。

Abstract

With the increasing number of social network users in recent years

text-based user sentiment analysis technology has been widely concerned and applied.However

data sparsity and low accuracy often reduce the accuracy and speed of emotion recognition methods.The user emotion Biterm topic model (US-BTM) was proposed which could find user preference and emotional tendency from the text of specific places

so as to effectively use Biterm for topic modeling.The strategy of user aggregation to form pseudo-documents was used

and word pairs were created for the whole corpus to solve the problems of data sparsity and short text.Then the topic was studied through the lexical co-occurrence model

so as to infer the topic with abundant corps-level information

and the purpose of accurately predicting the user’s interest

preference and emotion to the specific scene was achieved by analyzing the lexical matching set in the comment corpus under the specific scene and the emotion of the corresponding topic.The experimental results show that the method proposed can accurately capture users’ emotional tendency and correctly reveal users’ preference

which can be widely used in social network content description

recommendation

social network user interest description

semantic analysis and other fields.

关键词

Keywords

references

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