Question

我正在尝试训练朴素贝叶斯分类器来预测电影评论的好坏。我正在按照本教程进行操作，但是在尝试训练模型时遇到错误：

https://medium.com/@MarynaL/analyzing-movie-review-data-with-natural-language-processing-7c5cba6ed922

我一直遵循所有步骤，直到训练模型为止。我的数据和代码看起来像这样：

                                                 Reviews  Labels
0      For fans of Chris Farley, this is probably his...       1
1      Fantastic, Madonna at her finest, the film is ...       1
2      From a perspective that it is possible to make...       1
3      What is often neglected about Harold Lloyd is ...       1
4      You'll either love or hate movies such as this...       1
                                              ...     ...
14995  This is perhaps the worst movie I have ever se...       0
14996  I was so looking forward to seeing this film t...       0
14997  It pains me to see an awesome movie turn into ...       0
14998  "Grande Ecole" is not an artful exploration of...       0
14999  I felt like I was watching an example of how n...       0

gnb = MultinomialNB()
gnb.fit(all_train_set['Reviews'], all_train_set['Labels'])

但是，当尝试拟合模型时，出现此错误：

ValueError: could not convert string to float: 'For fans of Chris Farley, this is probably his best film. David Spade pl

如果有人可以帮助我确定为什么本教程出错了，将不胜感激。

非常感谢

Answer 1

实际上，在使用Scikit-learn之前，您必须先将文本转换为数字，然后再调用分类器。您可以通过使用CountVectorizer或TfidfVectorizer来做到这一点。

如果您想使用更现代的单词嵌入，可以使用Zeugma软件包（将其与pip install zeugma一起安装在终端中），例如

from zeugma.embeddings import EmbeddingTransformer

embedding = EmbeddingTransformer('glove')

X = embedding.transform(all_train_set['Reviews'])
y = all_train_set['Labels']

gnb = MultinomialNB()
gnb.fit(X, y)

希望对您有帮助！

朴素贝叶斯分类器不适用于情绪分析

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