Komparasi Model Analisis Sentimen Pada Twitter Terhadap Kemahalan Minyak Goreng dengan Metode Naive Bayes dan Support Vector Machine

Moh. Aminullah Al Fachri, Ummi Athiyah
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Abstract

At the end of 2021, people are shocked by the drastically reduced supply of cooking oil and high prices. This makes people talk about it a lot through social media like Twitter. Freedom on Twitter raises many responses from the public. The number of negative and positive responses on Twitter makes comparisons between the two responses difficult to observe. This study aims to determine the comparison of positive responses and negative responses. Machine learning with the naïve Bayes method and support vector machine is able to overcome this problem. The research conducted examines how the comparison between positive responses and negative responses and which method has higher accuracy. The data used is 10,000 Indonesian language tweets. Model testing was carried out with 1839 test data. the Naive Bayes method gets an accuracy of 74.06% with the results of predicting two positive tweets and 1837 negative tweets. The SVM method was tested on linear, polynomial, RBF, and sigmoid kernels. The kernel with the highest accuracy value is the sigmoid kernel with an accuracy of 81.8% with the predicted results of 266 positive tweets and 1573 negative tweets.  
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Twitter上对油墨不足的情绪分析与天真的贝耶(Naive Bayes)方法和支撑向量机(Support Vector Machine)的比较
2021年底,人们对食用油供应急剧减少和价格高企感到震惊。这使得人们通过像推特这样的社交媒体谈论很多。推特上的自由引起了公众的许多反应。推特上负面和正面回应的数量使得很难对这两种回应进行比较。本研究旨在确定积极反应和消极反应的比较。使用naïve贝叶斯方法和支持向量机的机器学习能够克服这个问题。研究考察了积极反应和消极反应的比较,以及哪种方法具有更高的准确性。使用的数据是10000条印尼语推文。利用1839个试验数据进行了模型试验。朴素贝叶斯方法预测正面推文2条,负面推文1837条,准确率为74.06%。支持向量机方法在线性、多项式、RBF和s型核上进行了测试。准确率最高的核是sigmoid核,准确率为81.8%,预测结果为266条正面推文和1573条负面推文。
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审稿时长
12 weeks
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