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Sentiment Analysis of YouTube Video Comments Using Deep Neural Networks

机译：使用深度神经网络的YouTube视频评论情感分析

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摘要

Over the years, social networks have become an important vehicle for communication. Many users on YouTube use comments to express opinions or critique a subject. The amount of comments, for famous videos and channels, is huge, which poses the challenge of analysing user opinions efficiently. This article proposes a sentiment analysis model of YouTube video comments, using a deep neural network. We employed an embedding layer to represent input text as a tensor, then we used a pair of convolutional layers to extract features and a fully connected layer to make the classification. The output of the neural network is the sentiment classification among negative, positive or neutral. Two videos were chosen and their comments were classified by our model, by an alternative statistical model and by humans. The human classification was considered to be 100% accurate. The results showed that our model achieves better accuracy than the statistical model, and the classification accuracy is in the range 60%-84%.
机译：多年来，社交网络已成为交流的重要工具。 YouTube上的许多用户都使用评论来表达意见或批评主题。对于著名的视频和频道，评论数量巨大，这带来了有效分析用户意见的挑战。本文提出了使用深度神经网络的YouTube视频评论情感分析模型。我们使用嵌入层将输入文本表示为张量，然后使用一对卷积层提取特征，并使用完全连接的层进行分类。神经网络的输出是消极，积极或中立的情感分类。选择了两个视频，并根据我们的模型，替代统计模型和人员对他们的评论进行了分类。人工分类被认为是100％准确的。结果表明，与统计模型相比，我们的模型具有更好的准确性，分类精度在60％-84％范围内。

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