基于连接规则的印尼泗水地区自来水公司服务投诉情绪分析

A. Anggraini, Entin Martiana Kusumaningtyas, Ali Ridho Barakbah, M. T. Fiddin Al Islami
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引用次数: 0

摘要

PDAM(区域饮用水公司)是一家提供清洁水的公司。PDAM通过考虑用户的投诉、建议和投诉来开发他们的服务。随着时间的推移,PDAM服务的用户在增加,因此投诉的数量也在增加,PDAM无法使用手动数据分析投诉数据。本文提出了基于规则的情感分析和分类方法对PDAM投诉数据进行分析的思路。本研究中基于规则的情感分析使用了12条规则,其中基于规则的独特性是一个检测连接。印度尼西亚连接检测是印度尼西亚第一种可用的方法。提出连词检测是为了发现连词对句子的意义是否有重要影响。情绪分析的结果是对投诉语句进行评分,分为消极、积极或中性。分类是一种提供句子分数的方法,包括对混浊、漏水、漏水、仪表、使用、不取水的投诉。对392个包含连词的数据进行了实验情绪分析,并对人工情绪进行了评分。使用基于连词检测的规则获得的准确率值比基于不使用连词检测的规则获得的准确率值提高13%。100条投诉数据的分类准确率值为84%为真,16%为假。因此,对于高准确率值,在连接检测中需要注意句子和单词字典的上下文,在分类中也必须注意单词并在程序中编写优先级类别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indonesian Conjunction Rule Based Sentiment Analysis For Service Complaint Regional Water Utility Company Surabaya
PDAM (Regional Drinking Water Company) is a company that provides clean water. PDAM develops their services with by considering complaints, suggestions and complaints from users. Over time PDAM services users are increasing thus allowing the number of complaints to also increase and PDAM is impossible to analyze the complaint data using the manual data. In this research proposes ideas to analyze PDAM complaints data with rule based sentiment analysis and categorization methods. The rule based sentiment analysis in this research used twelve rules, where the uniqueness of this rule based is a detection conjunction. Indonesian conjunction detection is the first method available in Indonesia. Detection of conjunction is proposed to find out whether conjunction has an important influence in the meaning of a sentence. The result of sentiment analysis is a score from complaint sentence are negative, positive or neutral. And categorization is a method to provide sentence score including complaints on turbid, leaky water, leakage, meters, usage, or not getting water. An experiment sentiment analysis was conducted on 392 data containing conjunctions and have score manually sentiment. The accuracy value obtained used rule based with conjunctions detection increases 13% than rule based do not use conjunction detection. And the accuracy value of categorization on 100 complaint data are 84% true and 16% false. So for High accuracy values in Conjunction detection needs to notice the context of the sentence and word dictionary and in categorization must also notice to words and write priority categories in the program.
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