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Study on network language viewpoint extraction based on affective computing

机译：基于情感计算的网络语言观点提取研究

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In recent years, the frequent occurrence of public sentiment has caught attention. In order to effectively control the incident, the study accurately extracted public views. The diverse content mainly includes the four main elements of public opinion (the theme, the holder, statement of holder and emotional tendencies of holder). By analyzing posts on internet forum, this study found that emotion tendencies are the key points of analysis. It is mainly about the qualitative analysis of tendency on post contents in forums. The content on the web forum discussion is varied with a variety of topics and witty cyber words. For the characteristics of this network language, the study manages to improve polarity dictionary by the extension of cyber word dictionaries, mainly to add some words used in network language experiments. The study attached great importance on the special words and special symbols???? influences in sentences, increasing the accuracy of the statement propensity analysis. An example is set to illustrate the problem of network language propensity: Content affective computing of theme A and theme B, if the article support A and oppose B, it means positive; otherwise it means negative. The study apples the above rules for affective computing, meanwhile statistical analyzes peoples???? emotional tendencies. This research achievement has great practical values.
机译：近年来，公众情绪的频繁发生引起了人们的关注。为了有效地控制事件，该研究准确地提取了公众意见。内容的多样性主要包括舆论的四个主要要素（主题，持有人，持有人的陈述和持有人的情感倾向）。通过分析互联网论坛上的帖子，本研究发现情绪倾向是分析的重点。主要是对论坛帖子内容趋势的定性分析。网络论坛讨论的内容因主题和机智的网络词汇而异。针对这种网络语言的特点，该研究设法通过扩展网络单词词典来改进极性词典，主要是添加一些用于网络语言实验的单词。该研究非常重视特殊词和特殊符号？？？？句子中的影响，提高了陈述倾向分析的准确性。通过一个例子来说明网络语言倾向性问题：主题A和主题B的内容情感计算，如果文章支持A而反对B，则表示肯定。否则就表示负面。该研究为情感计算确定了上述规则，同时统计分析了人们。情绪倾向。该研究成果具有很大的实用价值。

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