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首页> 外文期刊>International journal of knowledge and web intelligence >Determining the semantic orientation of opinion words using typed dependencies for opinion word senses and SentiWordNet scores from online product reviews
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Determining the semantic orientation of opinion words using typed dependencies for opinion word senses and SentiWordNet scores from online product reviews

机译:使用类型相关性来确定意见词的语义定向,这些类型取决于意见词的意义和在线产品评论中的SentiWordNet得分

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摘要

Opinion words express the information regarding the like and dislike of a user on the target entities such as products and product aspects present in the online reviews. The polarised information collected from the reviews is analysed by calculating the orientation of the adjectives. The synonymy relation graph is a way to determine the orientation of the adjectives present in the product reviews dataset. It considers the minimum path length between the adjectives under analysis using WordNet synsets. The synonymy relation graph cannot determine the orientations of all the opinion words present in the dataset. In order to evaluate opinion orientation of all the adjectives from the dataset, the synonymy relation graph of WordNet is to be replaced with the SentiWordNet scores of the opinion words. These scores are provided to the opinion words by finding the contextual clues surrounding the opinion words to disambiguate their sense. The contextual clues are finalised based on the typed dependencies grammatical relations. The distance between the opinion word and the context insensitive seed term (good/bad) is computed by calculating the difference between these scores. This paper addresses advantages of using SentiWordNet scores. This improves the accuracy of the determined opinion word orientations.
机译:观点词表示有关用户对目标实体的喜欢和不喜欢的信息,例如在线评论中存在的产品和产品方面。通过计算形容词的方位来分析从评​​论收集的极化信息。同义关系图是确定产品评论数据集中存在的形容词方向的一种方式。它考虑使用WordNet同义词集进行分析的形容词之间的最小路径长度。同义关系图无法确定数据集中存在的所有意见词的方向。为了从数据集中评估所有形容词的观点取向,WordNet的同义词关系图将替换为观点词的SentiWordNet分数。通过找到围绕意见词的上下文线索来消除歧义,可以将这些分数提供给意见词。根据类型化的依存关系语法关系来确定上下文线索。通过计算这些分数之间的差异,可以计算出意见词与上下文无关种子词(好/坏)之间的距离。本文介绍了使用SentiWordNet分数的优势。这提高了所确定的意见词方位的准确性。

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