Classifying the Polarity of Online Media on the Indonesia Presidential Election 2019 Using Artificial Neural Network

Muhammad Afif Farisi, K. Lhaksmana
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Abstract

The 2019 presidential election is one of the mandatory national agendas that is covered by all of the mainstream news media in Indonesia. The function of news media as an information provider reaps criticism because they are suspected of having polarity towards certain candidates. In this paper, the polarity of news media is analyzed by performing sentiment assessment towards every news regarding each candidate. Since manual sentiment analysis is costly and time-consuming, because of the large amount of data that needs to be processed, we adopt a machine learning method to automate the sentiment analysis process. This research employs Artificial Neural Network (ANN) to classify scraped news texts from online media and TF-IDF weighting method for feature extraction. We found that the observed online media kompas.com, liputan tan6.com, republika.co.id, and tempo.co do not have significant polarity toward one of the candidates. In addition to ANN, we also compared other methods to investigate the appropriate methods for our dataset. Our experiment shows that on average, ANN obtains the best accuracy at 84.57%, compares to Decision Tree C4.5 (83.34%), Naive Bayes (SO.42%), and SVM (79.04%).
基于人工神经网络的2019年印尼总统大选网络媒体极性分类
2019年的总统选举是印尼所有主流新闻媒体报道的强制性国家议程之一。新闻媒体作为信息提供者的功能受到了批评,因为它们被怀疑对某些候选人有两极分化。在本文中,通过对每个候选人的每条新闻进行情绪评估来分析新闻媒体的极性。由于人工情感分析成本高,耗时长,需要处理的数据量大,我们采用机器学习的方法实现情感分析过程的自动化。本研究采用人工神经网络(ANN)对网络媒体抓取的新闻文本进行分类,并采用TF-IDF加权法进行特征提取。我们发现观察到的网络媒体kompas.com, liputan tan6.com, republika.co。Id和节拍。我们对其中一个候选人没有明显的极性。除了人工神经网络,我们还比较了其他方法来研究适合我们数据集的方法。我们的实验表明,与决策树C4.5(83.34%)、朴素贝叶斯(SO.42%)和支持向量机(79.04%)相比,ANN的平均准确率为84.57%。
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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