Python Sentiment Analysis given a dataset with Facebook Posts
Question:
I have a dataset containing raw facebook posts and comments. What I would like to do is to perform sentiment analysis with Python 3 (NTLK ?) in order to label each post and each comment against some categories (a sort of clustering in unsupervised mode). The problem is that I have no idea how to do something similar.
I accept suggestions
Thank you
Answers:
You can use vaderSentiment which is a python package to perform unsupervised English Sentiment Analysis using dictionary and rules. There is some example on their github. This option might be more effective than an unsupervised clustering.
Can I ask where I downloaded this data, or is it possible to have a copy of this data? Because recently I also need such a user post data for analysis.
I have a dataset containing raw facebook posts and comments. What I would like to do is to perform sentiment analysis with Python 3 (NTLK ?) in order to label each post and each comment against some categories (a sort of clustering in unsupervised mode). The problem is that I have no idea how to do something similar.
I accept suggestions
Thank you
You can use vaderSentiment which is a python package to perform unsupervised English Sentiment Analysis using dictionary and rules. There is some example on their github. This option might be more effective than an unsupervised clustering.
Can I ask where I downloaded this data, or is it possible to have a copy of this data? Because recently I also need such a user post data for analysis.