网络舆论中隐性词汇的情感意涵研究Sentimental Connotations of Implicit Words in Public Opinion Online
盖赟,晏齐宏
摘要(Abstract)：
网络舆论研究大多关注显性情感，对隐性情感的分析较为缺乏。本研究使用加权词向量方法和双向长短时记忆网络(Bi-LSTM)方法进行情感多分类研究，进一步挖掘网络舆论中隐性词汇的情感意涵。研究首先以词频-逆文档频率(TF-IDF)理论思想为基础计算词向量的情感权重，然后对词向量进行情感加权，并将加权后的词向量输入Bi-LSTM模型进行特征计算，最后使用Softmax函数完成情感的多分类预测。同时通过回溯的方式，对情感权重值较高的代表性词汇(隐性词汇)进行情感意涵分析。结果表明，该方法不仅能够有效提升多类情感判别的准确率，还可以通过这些代表性词汇加深对隐性词汇中情感意涵的认识和把握，这对于我国这一具有自我审查文化传统的社会及其舆论的把握具有重要意义。
关键词(KeyWords)： 网络舆论;情感分析;隐性情感;双向长短时记忆网络
基金项目(Foundation): 教育部人文社会科学研究青年项目“基于大数据技术分析民众对党的十九大精神的舆情认知研究”(编号:18YJCZH030);; 中国社会科学院大学研究项目的阶段性成果
作者(Author): 盖赟,晏齐宏
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