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Sentiment Analysis of Indonesia’s National Economic Endurance using Fuzzy Ontology-Based Semantic Knowledge

机译：基于模糊本体的语义知识的印度尼西亚国家经济耐力的情感分析

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摘要

Campaigns of election candidates often enliven the social media platforms that they chose. The total number of social media users in Indonesia has reached approximately 130 million users. Making use of this momentum where the social media is very active in the year of campaigns and elections, we attempt to mine the public sentiment in terms of national endurance using fuzzy ontology-based semantic knowledge. A regular ontology is usually considered rather ineffective in extracting information from tweets; thus, we use the concept of fuzzy ontology-based semantic knowledge. Fuzzy ontology-based semantic knowledge is one method of sentiment analysis using combined approach of lexicon-based, ontology-based, and fuzzy logic. This method gives results whether a tweet is categorized as strong negative, negative, neutral, positive, or strong positive. Moreover, a regular ontology is unable to classify a tweet into sentiment categories when that tweet has more than one SentiWord value. From the 2032 tweets with sentiments, we found 205 tweets having more than one SentiWord values. Therefore, the application of FuzzyDL is needed to solve this problem. Using this method, we obtain the accuracy score of 78%, precision score of 93%, recall score of 73%, and F-measure score of 82%.
机译：选举候选人的竞选频率往往活跃于他们选择的社交媒体平台。印度尼西亚的社交媒体用户总数已达到约1.3亿用户。利用这种势头，社交媒体在活动和选举年度非常活跃，我们试图在利用模糊本体的语义知识的国家耐力方面挖掘公众情绪。常规本体通常被认为是从推文中提取信息时相当无效;因此，我们使用基于模糊本体的语义知识的概念。基于模糊的本体论的语义知识是利用基于词典，本体的组合方法和模糊逻辑的组合方法的一种情感分析方法。此方法提供了推文的结果，是否分类为强负，负，中性，积极或强烈的正面。此外，常规本体无法将Tweet分类为情绪类别，当推文有多个Sentiward值时。从2032个具有情绪的推文，我们发现了205​​个具有多个SentiWord值的推文。因此，需要使用Fuzzydl来解决这个问题。使用这种方法，我们获得了78％的精度得分，精度得分为93％，召回得分为73％，而F测量得分为82％。

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