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Automatic Detection of Political Opinions in Tweets

机译:自动检测推文中的政治观点

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摘要

In this paper, we discuss a variety of issues related to opin ion mining from microposts, and the challenges they impose on an NLP system, along with an example application we have developed to deter mine political leanings from a set of pre-election tweets. While there are a number of sentiment analysis tools available which summarise posi tive, negative and neutral tweets about a given keyword or topic, these tools generally produce poor results, and operate in a fairly simplistic way, using only the presence of certain positive and negative adjectives as indicators, or simple learning techniques which do not work well on short microposts. On the other hand, intelligent tools which work well on movie and customer reviews cannot be used on microposts due to their brevity and lack of context. Our methods make use of a variety of sophisticated NLP techniques in order to extract more meaningful and higher quality opinions, and incorporate extra-linguistic contextual information.
机译:在本文中,我们讨论了与微博中的观点挖掘相关的各种问题,以及它们对NLP系统带来的挑战,以及我们开发的示例应用程序,可以阻止一系列选举前推文中的政治倾向。虽然有很多情绪分析工具可以汇总关于给定关键字或主题的正面,负面和中性推文,但这些工具通常仅使用某些正面和负面的结果,产生的效果很差,并且以相当简单的方式操作形容词作为指标,或者简单的学习技巧在简短的微博上效果不佳。另一方面,由于微博的简洁性和缺乏上下文,无法在微博上使用在电影和客户评论上都能正常使用的智能工具。我们的方法利用了各种复杂的NLP技术,以提取出更有意义和更高质量的意见,并纳入额外的语言环境信息。

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