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Exploiting social and local contexts propagation for inducing Chinese microblog-specific sentiment lexicons

机译:利用社会和地方环境的传播来诱导中国微博特定的情感词典

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Sentiment lexicons including opinion words, sentiment phrases, and idioms with sentiment polarities play an important role in sentiment analysis tasks. Apart from explicit sentiment features, extracting implicit sentiment features is a challenging research issue. The sentiment expression is very domain-specific, and constructing a general sentiment lexicon that is suitable for all domains is hard or even impossible. In this paper, we propose a novel sentiment unit context propagation framework to extract Chinese microblog-specific explicit and implicit sentiment features. In the process of the selection of seed sentiment units, we select the seed sentiment units that have a large standard degree of centrality with other units, and mark these units with sentiment labels using general sentiment lexicons and manual calibrations. To realize sentiment label propagation from a small amount of labeled sentiment units to unlabeled ones, we exploit local contexts, topic features, and social relationships among users in microblog social networks. After that, the sentiment scores of units are calculated using unit context sentiment propagation. Experiments on two real-world microblog data sets demonstrate that our method can generate microblog-specific sentiment lexicons effectively. Furthermore, the sentiment classification accuracies significantly outperform state-of-the-art baselines. (C) 2018 Elsevier Ltd. All rights reserved.
机译:情感词汇(包括见义词,情感短语和带有情感极性的成语)在情感分析任务中起着重要作用。除了显式的情绪特征之外,提取隐式的情绪特征是一个具有挑战性的研究问题。情感表达是非常特定于领域的,并且构建适用于所有领域的通用情感词典非常困难,甚至不可能。在本文中,我们提出了一种新颖的情感单元上下文传播框架,以提取特定于中国微博的显性和隐性情感特征。在选择种子情感单元的过程中,我们选择与其他单元具有较高标准中心度的种子情感单元,并使用常规情感词典和手动校准在情感单元上标记这些单元。为了实现情感标签从少量标记的情感单元传播到未标记的情感单元,我们在微博社交网络中利用了本地上下文,主题特征以及用户之间的社交关系。之后,使用单元上下文情感传播来计算单元的情感分数。在两个真实世界的微博客数据集上进行的实验表明,我们的方法可以有效地生成特定于微博客的情感词典。此外,情感分类的准确性明显优于最新的基准。 (C)2018 Elsevier Ltd.保留所有权利。

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