Background/introduction

Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations.

Methods

This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an extended set of linguistic rules for concept-feature pair extraction along with enhanced set ontological relations. Additionally, a supervised a machine learning technique is implemented for performing concept-level sentiment analysis.

Results and conclusions

Experimental results depict the effectiveness of the proposed system in terms of improved efficiency (P: 88%, R: 88%, F-score: 88%, and A: 87.5%).

中文翻译：

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增强的概念级情感分析系统，具有扩展的本体关系，可有效分类用户评论

背景/介绍

概念级情感分析处理从在线表达的关于产品和其他实体（如政治领导人、政府政策等）的用户评论中提取和分类概念和特征。先前关于概念级情感分析的研究使用了一组有限的语言规则来提取概念及其相关特征。此外，早期工作中用于执行概念级情感分析的本体关系需要在特征概念和本体关系的扩展集方面进行增强。

方法

这项工作旨在解决上述问题，并试图通过提出用于概念-特征对提取的扩展语言规则集以及增强的本体关系集来弥合文献差距。此外，还实施了有监督的机器学习技术来执行概念级情感分析。

结果和结论

实验结果描述了所提出系统在提高效率方面的有效性（P：88%，R：88%，F-score：88%，A：87.5%）。