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FVEC feature and Machine Learning Approach for Indonesian Opinion Mining on YouTube Comments

机译:FVEC功能和机器学习方法,用于在YouTube评论上进行印尼观点挖掘

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Mining opinions from Indonesian comments from YouTube videos are required to extract interesting patterns and valuable information from consumer feedback. Opinions can consist of a combination of sentiments and topics from comments. The features considered in the mining of opinion become one of the important keys to getting a quality opinion. This paper proposes to utilize FVEC and TF-IDF features to represent the comments. In addition, two popular machine learning approaches in the field of opinion mining, i.e., SVM and CNN, are explored separately to extract opinions in Indonesian comments of YouTube videos. The experimental results show that the use of FVEC features on SVM and CNN achieves a very significant effect on the quality of opinions obtained, in term of accuracy.
机译:需要从YouTube视频中的印尼评论中挖掘观点,以便从消费者的反馈中提取出有趣的模式和有价值的信息。意见可以包含情绪和评论主题。挖掘意见中考虑的特征成为获取质量意见的重要关键之一。本文提出利用FVEC和TF-IDF功能来表示注释。另外,分别探索了两种在观点挖掘领域中流行的机器学习方法,即SVM和CNN,以从YouTube视频的印度尼西亚评论中提取观点。实验结果表明,就准确性而言,在SVM和CNN上使用FVEC功能对获得的意见质量具有非常重要的影响。

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