September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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第六章：在矛盾的線上評論中偵測謊言以及混亂的狀況
除非你很想要知道這些程式碼是如何運作，或是想要使用客製化的特徵值抽出方法，要
不然我強烈的建議在嘗試建立以 NLTK 為基礎的客製化分類器，或是任何的 NLTK 模型
之前，先嘗試這些套件。
驗證分類器
回到情緒分類器：不論統計是如何說，這些年來我學到的是，不要完全相信訓練模型以
及它們的輸入資料。除非每個訓練實體都被三個專業的評論者親眼看過，要不然你便能
夠假設在訓練資料中會擁有一些雜訊以及不準確的狀況。所以當我訓練好了一個應該要
是很準確的情緒分類器時，我會將它用在我的訓練語料庫上，來看看我是否能夠發現被
該分類器分錯的評論。我的目標是想要理解分類器是哪裡出了錯，並且或許也能夠得到
一些要如何調整訓練參數而有更好結果的靈感。讓我驚訝的是，我發現有一則評論是被
分類在
neg
之下，但其實它是在 pos ∕ 5 顆星的分類中：
它很吵且一杯酒的價錢太貴。這是它唯一的缺點，因為它很好。
被分類在
pos
之下，但其實是在 neg ∕ 1 顆星的分類中的評論如下：
在紐約中，其中一個最佳的享受浪漫夜晚的地點。在合理的價位下擁有非常
好的食物以及服務。
我們愛它！服務生很棒、上菜速度很快而且又好吃。我們給 5 顆星！
結果，分類器在偵測情緒上，反而比用來建立訓練語料庫的評分還要準確！顯然的，其
中一個人們在線上做的怪事，就是撰寫與評分完全相反的評論。當我們試圖建立一個情
緒分類器時，我也意外的建立了一個辨識說謊以及混亂狀況的方法。以下便是一些說謊
者的 1 顆星評論： ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.