R:Naives贝叶斯分类器仅基于先验概率决定

Question

我试图根据他们的情绪将推文分为三类(买入,持有,卖出).我正在使用R和e1071包.

我有两个数据框:一个训练集和一组新推文,需要预测情绪.

训练集数据框:

   +--------------------------------------------------+

   **text | sentiment**

   *this stock is a good buy* | Buy

   *markets crash in tokyo* | Sell

   *everybody excited about new products* | Hold

   +--------------------------------------------------+

现在我想使用推文文本trainingset[,2]和情绪类别来训练模型 trainingset[,4].

classifier<-naiveBayes(trainingset[,2],as.factor(trainingset[,4]), laplace=1)

用分析查看分类器的元素

classifier$tables$x

我发现条件概率是计算出来的.每一条有关买入,持有和卖出的推文都有不同的概率.太好了.

但是当我预测训练集时:

predict(classifier, trainingset[,2], type="raw")

我得到的分类仅基于先验概率,这意味着每条推文都被归类为Hold(因为"Hold"在情绪中占有最大份额).所以每条推文都有相同的买入,持有和卖出概率:

      +--------------------------------------------------+

      **Id | Buy | Hold | Sell**

      1  |0.25 | 0.5  | 0.25

      2  |0.25 | 0.5  | 0.25

      3  |0.25 | 0.5  | 0.25

     ..  |..... | ....  | ...

      N  |0.25 | 0.5  | 0.25

     +--------------------------------------------------+

我有什么想法我做错了吗？感谢您的帮助!

谢谢

Answer 1

看起来您使用整个句子作为输入来训练模型,而您似乎希望使用单词作为输入要素.

用法:

## S3 method for class 'formula'
naiveBayes(formula, data, laplace = 0, ..., subset, na.action = na.pass)
## Default S3 method:
naiveBayes(x, y, laplace = 0, ...)


## S3 method for class 'naiveBayes'
predict(object, newdata,
  type = c("class", "raw"), threshold = 0.001, ...)

参数:

  x: A numeric matrix, or a data frame of categorical and/or
     numeric variables.

  y: Class vector.

特别是,如果你这样训练naiveBayes:

x <- c("john likes cake", "marry likes cats and john")
y <- as.factor(c("good", "bad")) 
bayes<-naiveBayes( x,y )

你得到一个能够识别这两句话的分类器:

Naive Bayes Classifier for Discrete Predictors

Call:
naiveBayes.default(x = x,y = y)

A-priori probabilities:
y
 bad good 
 0.5  0.5 

Conditional probabilities:
            x
      x
y      john likes cake marry likes cats and john
  bad                0                         1
  good               1                         0

要实现单词级别分类器,您需要使用单词作为输入来运行它

x <-             c("john","likes","cake","marry","likes","cats","and","john")
y <- as.factors( c("good","good", "good","bad",  "bad",  "bad", "bad","bad") )
bayes<-naiveBayes( x,y )

你得到

Naive Bayes Classifier for Discrete Predictors

Call:
naiveBayes.default(x = x,y = y)

A-priori probabilities:
y
 bad good 
 0.625 0.375 

Conditional probabilities:
      x
y            and      cake      cats      john     likes     marry
  bad  0.2000000 0.0000000 0.2000000 0.2000000 0.2000000 0.2000000
  good 0.0000000 0.3333333 0.0000000 0.3333333 0.3333333 0.0000000

一般来说R不太适合处理NLP数据,python(或者至少Java)会是更好的选择.

要将句子转换为单词,您可以使用该strsplit功能

unlist(strsplit("john likes cake"," "))
[1] "john"  "likes" "cake"