@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://preview.aclanthology.org/fix-sig-urls/2022.ccl-1.64/",
    pages = "716--727",
    language = "zho",
    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。''"
}