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Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis

机译：Sentic LDA：基于语义的语义分析在LDA方面的改进

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The advent of the Social Web has provided netizens with new tools for creating and sharing, in a time- and cost-efficient way, their contents, ideas, and opinions with virtually the millions of people connected to the World Wide Web. This huge amount of information, however, is mainly unstructured as specifically produced for human consumption and, hence, it is not directly machine-processable. In order to enable a more efficient passage from unstructured information to structured data, aspect-based opinion mining models the relations between opinion targets contained in a document and the polarity values associated with these. Because aspects are often implicit, however, spotting them and calculating their respective polarity is an extremely difficult task, which is closer to natural language understanding rather than natural language processing. To this end, Sentic LDA exploits common-sense reasoning to shift LDA clustering from a syntactic to a semantic level. Rather than looking at word co-occurrence frequencies, Sentic LDA leverages on the semantics associated with words and multi-word expressions to improve clustering and, hence, outperform state-of-the-art techniques for aspect extraction.
机译：社交网络的出现为网民提供了新的工具，可以以节省时间和成本的方式创建和共享其内容，思想和观点，并与数以百万计的连接到万维网的人们共享。但是，这种大量的信息主要是非结构化的，是专门为人类消费而生成的，因此，它不是直接可机加工的。为了使从非结构化信息到结构化数据的传输更加有效，基于方面的意见挖掘对文档中包含的意见目标与与之相关的极性值之间的关系进行建模。但是，由于各方面通常是隐式的，因此发现它们并计算其各自的极性是一项极其困难的任务，它更接近于自然语言理解而不是自然语言处理。为此，Sentic LDA利用常识推理将LDA聚类从句法转移到语义层次。 Sentic LDA不用关注单词共现的频率，而是利用与单词和多单词表达相关的语义来改善聚类，因此优于方面提取方面的最新技术。

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