您的问题非常基础，您设置的问题有误.理想情况下，您希望将 50-50 的正面和负面数据拆分为您的训练数据.由于朴素贝叶斯分类器的工作方式，它试图最小化熵.

Your issue is very basic, you are setting up your problem wrong. Ideally you want a 50-50 split of positives and negatives for your training data. Because of how the Naive Bayes classifier works, it is trying to minimize entropy.

我猜在您只有 1 条正面评论的情况下，分类器能够根据多个预测变量轻松地最小化熵.

I am guessing that in your case where you have only 1 positive comment, the classifier was able to minimize entropy very easily based on multiple predictors.

在您绝对不使用正面评论的情况下，您基本上是在说唯一的预测值/唯一可能的结果是悲伤"，而这正是您的模型所做的.

Where you use absolutely no positive comments, you are basically saying that the only predicted value/ the only possible outcome is "sad" and that is exactly what your model is doing.

至于您的主要问题，请使用不同的数据集尝试不同的问题.你从哪里得到你的推文，它们是否足够多样化?

As for your main issue, try a different using a different data set. Where are you getting your tweets from, are they sufficiently diverse?