I'll suggest to test the sentence or the tweet for polarity. This can be done using the textblob library. It can be installed as pip install -U textblob. Once the text data polarity is found, it can be assigned as a separate column in the dataframe. Subsequently, the sentence polarity can then be used for further analysis.
Polarity and Subjectivity are ...
First about the features i think you could add some such as :
the time when the letter is received,
number of links in the email,
the whole structure (does it follow typical structure for email),
number of words that contains numbers in it,
what is the whole mood of the email (sales,threats,info,...-for this purpose you can use sentiment analysis),
I understand that you are trying to derive new informative feature from the available tweet texts. And you do it in two steps: first you calculate dummy binary features, next you want to aggregate all binary features into one numerical feature.
Several aggregation rules come to mind:
simply calculate the sum of all binary features (and multiply by -5 if you ...
Manually assigning a value to a feature level can be done. However, it is often better to allow the machine learning algorithm to learn the importance of different features during the training process.
The general machine learning process starts with labeled data. If the labels are numeric, it is a regression problem. In the specific case of fake tweets, a ...