# How to consider the effect of exclamation marks in sentiment analysis

For example in the following two tweets, we can see the first one seems to be more negative than the second one:

1. "You are not required to come here now!!!"

2. "You are not required to come here now"

What are some ways to count the effect of exclamation marks so that we get better results?

In the following tweet:

"He is angry!!!"

We can keep "angry" two times, i.e., the tweet becomes:

"He is angry angry."

So suppose we are using positive/negative frequency model for sentiment analysis, then it's more likely that the word "angry" will be considered negatively because of its higher frequency in negative tweets.