@inproceedings{liu-etal-2022-ji,
title = "基于情感增强非参数模型的社交媒体观点聚类(A Sentiment Enhanced Nonparametric Model for Social Media Opinion Clustering)",
author = "Liu, Kan and
Chen, Yu and
He, Jiarui",
editor = "Sun, Maosong and
Liu, Yang and
Che, Wanxiang and
Feng, Yang and
Qiu, Xipeng and
Rao, Gaoqi and
Chen, Yubo",
booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Nanchang, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2022.ccl-1.64",
pages = "716--727",
abstract = "{``}本文旨在使用文本聚类技术,将社交媒体文本根据用户主张的观点汇总,直观呈现网民群体所持有的不同立场。针对社交媒体文本模式复杂与情感丰富等特点,本文提出使用情感分布增强方法改进现有的非参数短文本聚类算法,以高斯分布建模文本情感,捕获文本情感特征的同时能够自动确定聚类簇数量并实现观点聚类。在公开数据集上的实验显示,该方法在多项聚类指标上取得了超越现有模型的聚类表现,并在主观性较强的数据集中具有更显著的优势。{''}",
language = "Chinese",
}
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<abstract>“本文旨在使用文本聚类技术,将社交媒体文本根据用户主张的观点汇总,直观呈现网民群体所持有的不同立场。针对社交媒体文本模式复杂与情感丰富等特点,本文提出使用情感分布增强方法改进现有的非参数短文本聚类算法,以高斯分布建模文本情感,捕获文本情感特征的同时能够自动确定聚类簇数量并实现观点聚类。在公开数据集上的实验显示,该方法在多项聚类指标上取得了超越现有模型的聚类表现,并在主观性较强的数据集中具有更显著的优势。”</abstract>
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%0 Conference Proceedings
%T 基于情感增强非参数模型的社交媒体观点聚类(A Sentiment Enhanced Nonparametric Model for Social Media Opinion Clustering)
%A Liu, Kan
%A Chen, Yu
%A He, Jiarui
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G Chinese
%F liu-etal-2022-ji
%X “本文旨在使用文本聚类技术,将社交媒体文本根据用户主张的观点汇总,直观呈现网民群体所持有的不同立场。针对社交媒体文本模式复杂与情感丰富等特点,本文提出使用情感分布增强方法改进现有的非参数短文本聚类算法,以高斯分布建模文本情感,捕获文本情感特征的同时能够自动确定聚类簇数量并实现观点聚类。在公开数据集上的实验显示,该方法在多项聚类指标上取得了超越现有模型的聚类表现,并在主观性较强的数据集中具有更显著的优势。”
%U https://aclanthology.org/2022.ccl-1.64
%P 716-727
Markdown (Informal)
[基于情感增强非参数模型的社交媒体观点聚类(A Sentiment Enhanced Nonparametric Model for Social Media Opinion Clustering)](https://aclanthology.org/2022.ccl-1.64) (Liu et al., CCL 2022)
ACL