sentimentpy is designed to quickly calculate text polarity sentiment at the sentence level. The user can aggregate these scores by grouping variable(s) using built-in aggregate functions.
sentimentpy (a Python port of the R sentimentr package) is a response to my own needs with sentiment detection that were not addressed by the current R tools. My own polarity function in the R qdap package is slower on larger data sets. It is a dictionary lookup approach that tries to incorporate weighting for valence shifters (negation and amplifiers/deamplifiers). Matthew Jockers created the syuzhet R package that utilizes dictionary lookups for the Bing, NRC, and Afinn methods as well as a custom dictionary. He also utilizes a wrapper for the Stanford coreNLP which uses much more sophisticated analysis. Jocker's dictionary methods are fast but are more prone to error in the case of valence shifters. Jocker's addressed these critiques explaining that the method is good with regard to analyzing general sentiment in a piece of literature. He points to the accuracy of the Stanford detection as well. In my own work I need better accuracy than a simple dictionary lookup; something that considers valence shifters yet optimizes speed which the Stanford's parser does not. This leads to a trade off of speed vs. accuracy. Simply, sentimentpy attempts to balance accuracy and speed.
Currently, this is a GitHub package. To install use:
pip install git+https://github.com/trinker/sentimentpy
import sentimentpy.split_sentences as ss
s = [
' I like you. P.S. I like carrots too mrs. dunbar. Well let\'s go to 100th st. around the corner. ',
'Hello Dr. Livingstone. How are you?',
'This is sill an incomplete thou.'
]
ss.split_sentences(s)
['I like you.', 'P.S. I like carrots too mrs. dunbar.', "Well let's go to 100th st. around the corner.", 'Hello Dr. Livingstone.', 'How are you?', 'This is sill an incomplete thou.']
x = [
" ".join(
["Mr. Brown comes! He says hello. i give him coffee. i will ",
"go at 5 p. m. eastern time. Or somewhere in between!go there"
]),
" ".join(
["Marvin K. Mooney Will You Please Go Now!", "The time has come.",
"The time has come. The time is now. Just go. Go. GO!",
"I don't care how."
])
]
ss.split_sentences(x)
['Mr. Brown comes!', 'He says hello.', 'i give him coffee.', 'i will go at 5 p.m. eastern time.', 'Or somewhere in between!', 'go there', 'Marvin K. Mooney Will You Please Go Now!', 'The time has come.', 'The time has come.', 'The time is now.', 'Just go.', 'Go.', 'GO!', "I don't care how."]