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澳洲麦考瑞作业代写 推特

Keywords:澳洲麦考瑞作业代写 推特

Twitter是一个全球性的传播网络,目的是传播更多的受众。twitter数据的规模和范围催生了预测社交媒体数据结果的方法创新。在twitter上,人们发一条不超过140个字符的短消息。Topic是tweet中的一个短语,例如选举日是不同tweet中的一个主题。热门话题是一个变得流行的话题。Twitter为我们提供了关于热门话题的信息,但随着此类数据的生产速度不断增加,我们需要克服一些定量问题,比如更快地执行算法以获得更好的性能,以及质量问题,比如更好地呈现相关数据。Twitter热门话题可以看作是一个时间序列问题。训练数据是一小部分的所有微博动态意味着创建一个数据集和测试数据从数据实时tweets努力早些时候在趋势检测问题作为一个时间序列的分类问题,通过主题分类的欧几里得距离主题时间序列。仅仅流行是不够的,一个话题是流行的,twitter也认为新鲜比流行更重要。应该注意的是围绕特定话题的交流节奏。twitter趋势预测研究的先驱(Stanislav Nikolov和Devavrat Shah,麻省理工学院,2012)提出了一种非参数方法(模型参数范围与数据)来比较实时tweet,该方法使用了使用距离计算加权的机器学习算法。推特宣布巴克莱在2012年有上升趋势。研究人员对该数据集做了一个算法,它已经成为趋势,并发现它在一个小时前已经成为趋势。在趋势主题上,模型应该声明它是趋势,而在非趋势主题上,模型不应该声明它们正在成为趋势。算法的成功率为95%,错误率为4%。他们使用了250个趋势和非趋势类别的数据集进行测试。该模型还成功预测了2012年美国小姐奥利维亚·卡尔波(Olivia Culpo)将在成为推特热门话题之前成为热门话题。非参数算法方法的输出如图1所示。
澳洲麦考瑞作业代写 推特
Twitter is a global communication network to communicate a larger audience. The scale and scope of twitter data has given rise to design methodological innovations to predict social media data outcomes. In twitter people tweet in a short message no more than 140 characters. Topic is a phrase in the tweet, for example election day is a topic in different tweets. A trending topic is a topic that becomes popular. Twitter gives us information about the trending topics but as the rate of production of such data increases continuously overtime, we need to overcome quantitative issues like faster execution of the algorithm for better performance and qualitative issues like better presentation of relevant data. Twitter trending topic can be considered as a time series problem. The training data is a small percentage of all the tweets in a dataset and test data is dynamic means created from real time tweets data Earlier efforts has made in trend detection problem as a time series classification problem, by making topic classification with Euclidean distance of the topic time series. Popularity only, is not enough for a topic to be trending, twitter also considers newness over popularity. It is the tempo of communication around a specific topic which should be observed.The pioneers in the research of twitter trend prediction (Stanislav Nikolov and Devavrat Shah,MIT,2012) proposed a nonparametric approach (model parameters extent with data) to compare real-time tweets which applies machine learning algorithms using distance calculations weighted. Twitter announced Barclays is trending in 2012.Reserchers have done an algorithm on that dataset, that had already become trending and found that it has become trending an hour before. On trending topics, the model should declare that it is trending and on non-trending topic model should not declare that they are becoming trending. The algorithm success rate was 95% and error rate 4%. They have used 250 datasets of trending and non-trending category for the testing. The model has also successfully predicted Miss USA 2012 Olivia Culpo will become trending before it has become trending in Twitter. The output of non-parametric algorithm approach is shown in figure1.

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