银行服务质量的情感分析:基于规则的分类器

2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT) Pub Date : 2016-10-01 DOI:10.1109/ICAICT.2016.7991688

Yuliya Bidulya, Elena G. Brunova

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引用次数: 12

摘要

本文考虑对用户生成内容中的主观信息进行分析。本研究的目的是开发一个基于规则的分类器，用于银行服务质量领域的情感分析。我们的情感词汇包括286个积极词汇和385个消极词汇。此外，还增加了三个词汇类;它们需要应用基于规则的算法。为了测试该算法，我们分析了200条俄语评论。实验表明，与Naïve贝叶斯分类器相比，基于规则的分类器的效率更高。可以确定的是，系统通常会更好地检测正面评论而不是负面评论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Sentiment analysis for bank service quality: A rule-based classifier

The paper considers the analysis of the subjective information from user-generated content. The purpose of this research is to develop a rule-based classifier for the sentiment analysis within the bank service quality domain. Our sentiment lexicon includes 286 positive and 385 negative words. Besides, three more lexicon classes are added; they are required to apply the rule-based algorithm. To test the algorithm, 200 reviews in Russian are analyzed. The experiment demonstrates that the efficiency of the rule-based classifier is higher as compared to the Naïve Bayes classifier. It is determined that the system generally detects positive reviews better than negative ones.

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2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT)

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