Python Social Media Analytics

K-fold cross-validation

The input data is split into K parts where one is reserved for testing, and the other K-1 for training. This process is repeated K times and the evaluation metrics are averaged. This helps in determining how well a model would generalize to new datasets.

In our example, we have labeled 96 observations in three classes (positive, negative, and neutral). We used 80 as a training set and 16 observations (17%) as a test set. Many tweets are ambiguous for sentiment classification even for human beings. Therefore, we would expect the performance in terms of precision of around 80%.

We have split our tests into three parts:

• Training set 83% - Test set 17%
• Cross validation
• Qualitative verbatim evaluation

 print("Naive Bayes") ...