Sentiment Analysis of Indonesian News Using Deep Learning (Case Study: TVKU Broadcast)

机译：深度学习的印度尼西亚新闻的情感分析（案例研究：TVKU广播）

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A news sentence is a sentence that tells or does something. In fact, The comparison between positive news and negative news broadcast on TV stations in Indonesia is 1:11. One point for positive newscasting and 11 points for negative newscasting. In the TVKU dataset because the original record number is too large, then the dataset used in this study only used 2.048 data. Which 1025 Positif and 1023 negative. Then obtained accurate results between labeling and testing. By using deep learning, we can get accurate results to analyze the sentiment from TVKU news.

机译：新闻判决是一个句子，讲述或做某事。事实上，印度尼西亚电视台上的积极新闻和负面新闻之间的比较是1:11。积极新闻的一点，负面新闻发布的11分。在TVKU数据集中，因为原始记录号太大，那么本研究中使用的数据集仅使用2.048数据。哪个1025天赋和1023负。然后在标签和测试之间获得准确的结果。通过使用深度学习，我们可以获得准确的结果来分析TVKU新闻的情绪。

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来源
《International Seminar on Application for Technology of Information and Communication》|2018年|616p|共5页
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作者
Nurkholis; Meila Zuwari Negara; Guruh Fajar Shidik; A. Zainul Fanani; Muljono; Edi Noersasongko;
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中图分类 TN91-53;
关键词
Sentiment analysis; Testing; Text mining; Data models; Predictive models; TV;

机译：情绪分析;测试;文本挖掘;数据模型;预测模型;电视;

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Sentiment Analysis of Indonesian News Using Deep Learning (Case Study: TVKU Broadcast)

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