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澳洲麥考瑞作業代寫 推特

Keywords:澳洲麥考瑞作業代寫 推特

Twitter是一個全球性的傳播網絡,目的是傳播更多的受眾。twitter數據的規模和范圍催生了預測社交媒體數據結果的方法創新。在twitter上,人們發一條不超過140個字符的短消息。Topic是tweet中的一個短語,例如選舉日是不同tweet中的一個主題。熱門話題是一個變得流行的話題。Twitter為我們提供了關于熱門話題的信息,但隨著此類數據的生產速度不斷增加,我們需要克服一些定量問題,比如更快地執行算法以獲得更好的性能,以及質量問題,比如更好地呈現相關數據。Twitter熱門話題可以看作是一個時間序列問題。訓練數據是一小部分的所有微博動態意味著創建一個數據集和測試數據從數據實時tweets努力早些時候在趨勢檢測問題作為一個時間序列的分類問題,通過主題分類的歐幾里得距離主題時間序列。僅僅流行是不夠的,一個話題是流行的,twitter也認為新鮮比流行更重要。應該注意的是圍繞特定話題的交流節奏。twitter趨勢預測研究的先驅(Stanislav Nikolov和Devavrat Shah,麻省理工學院,2012)提出了一種非參數方法(模型參數范圍與數據)來比較實時tweet,該方法使用了使用距離計算加權的機器學習算法。推特宣布巴克萊在2012年有上升趨勢。研究人員對該數據集做了一個算法,它已經成為趨勢,并發現它在一個小時前已經成為趨勢。在趨勢主題上,模型應該聲明它是趨勢,而在非趨勢主題上,模型不應該聲明它們正在成為趨勢。算法的成功率為95%,錯誤率為4%。他們使用了250個趨勢和非趨勢類別的數據集進行測試。該模型還成功預測了2012年美國小姐奧利維亞·卡爾波(Olivia Culpo)將在成為推特熱門話題之前成為熱門話題。非參數算法方法的輸出如圖1所示。
澳洲麥考瑞作業代寫 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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