Train/Test dataset and model
You want to manually label some cases and then extend that &quot;manual labeling&quot; to the rest of the data.
This is a supervised learning excercise with prior manual labeling by you.
Let's suppose you have partitioned a random, suitably sized training data set. Now you need to model a classification algorithm via the classical modeling pipeline and use this model to predict the classifaction/label in the rest of the data.
So yes this is easily possible,however modeling a text classification model is non-trivial and you need to understand basic modeling.
Here are the basic steps, please read upon each step you do not know how to do:

Split your data into a training set (you will model on this data), a test set and a target set.

Manually label your training and test set.

Choose the kind of classification algorithm you want to use. You can use classical ML models but this involves heavily tokenizing and transforming your data to numerical components. You could also use more advanced deep learning techniques for text classification like BERT.

Create a suitable transformer to tidy and transform your data into the right format for your chosen algorithm.

Train the model on your manually labeled training data.

Evaluate and optimize your performance with the test data set.

Use the final model to predict labels in your target set.


Understand that the quality of the automatic labeling will only be as good as your manual labels.