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Sentence-Level Sentiment Polarity Classification Using a Linguistic Approach

机译：使用语言学方法的句子级情感极性分类

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Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing as this provides a refined analysis on the grammar and semantics of the textual data, which could improve performance. However, typed dependencies only provide the grammatical relationships between individual words while there exist more complex relationships between words that could influence a sentence sentiment polarity. In this paper, we propose a linguistic approach, called Polarity Prediction Model (PPM), that combines typed dependencies and subjective phrase analysis to detect sentence-level sentiment polarity. Our approach also considers the intensity of words and domain terms that could influence the sentiment polarity output. PPM is shown to provide a fine-grained analysis for handling and explaining the complex relationships between words in detecting a sentence sentiment polarity. PPM was found to consistently outperform a baseline model by 5% in terms of overall Fl-score, and exceeding 10% in terms of positive Fl-score when compared to a Typed-dependency only approach.
机译：最近的情感分析研究集中在使用类型相关性分析的单词的功能关系上，因为这为文本数据的语法和语义提供了精细的分析，可以提高性能。但是，类型相关性仅提供单个单词之间的语法关系，而单词之间存在更复杂的关系，这可能会影响句子的情感极性。在本文中，我们提出了一种称为极性预测模型（PPM）的语言方法，该方法结合了类型依赖性和主观短语分析来检测句子级情感极性。我们的方法还考虑了可能影响情感极性输出的单词和领域术语的强度。 PPM显示为处理和解释检测句子情感极性中单词之间的复杂关系提供了细粒度的分析。与仅依赖类型的方法相比，发现PPM的总体Fl评分始终优于基线模型，而总的Fl评分则超过10％。

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