Sentiment Analysis can be used to understand human emotions and psychology using their feedback, comments or posts from social media network or any other survey platform.
Below is a very simple example in Python where just in few lines of code we could take a string input and derive a polarity out of it. I used textblob for this.
The output will be a polarity in the range of -1 to +1
-1 being negative sentiment and + 1 is very positive sentiment .
I tested 3 cases as below
a) "Last night food was good, but it was expensive"
The above sentiment resulted in a polarity of 0.06
b) "Last night food was good"
This one resulted in 0.35
c) "I went for running, it felt awesome"
This resulted in 1
Below is a very simple example in Python where just in few lines of code we could take a string input and derive a polarity out of it. I used textblob for this.
The output will be a polarity in the range of -1 to +1
-1 being negative sentiment and + 1 is very positive sentiment .
I tested 3 cases as below
a) "Last night food was good, but it was expensive"
The above sentiment resulted in a polarity of 0.06
b) "Last night food was good"
This one resulted in 0.35
c) "I went for running, it felt awesome"
This resulted in 1
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