Welcome to Python Sentiment Analysis documentation!¶
Sentiment Analysis in Python using a Dictionary Approach
To get started, look here.
An overview¶
This is a library for sentiment analysis in dictionary framework. Two dictionaries are provided in the library, namely, Harvard IV-4 and Loughran and McDonald Financial Sentiment Dictionaries, which are sentiment dictionaries for general and financial sentiment analysis.
See also http://www.wjh.harvard.edu/~inquirer/ and https://www3.nd.edu/~mcdonald/Word_Lists.html .
Introduction¶
Positive and Negative are word counts for the words in positive and negative sets.
Polarity and Subjectivity are calculated in the same way of Lydia system.
See also http://www.cs.sunysb.edu/~skiena/lydia/
The formula for Polarity is,
Polarity= (Pos-Neg)/(Pos+Neg)
The formula for Subjectivity is,
Subjectivity= (Pos+Neg)/count(*)
Install¶
pip install pysentiment2
Usage¶
To use the Harvard IV-4 dictionary, create an instance of the HIV4 class
>>> import pysentiment2 as ps
>>> hiv4 = ps.HIV4()
>>> tokens = hiv4.tokenize(text) # text can be tokenized by other ways
# however, dict in HIV4 is preprocessed
# by the default tokenizer in the library
>>> score = hiv4.get_score(tokens)
HIV4 is a subclass for pysentiment2.base.BaseDict. BaseDict can be inherited by implmenting init_dict to initialize _posset and _negset for the dictionary
to calculate ‘positive’ or ‘negative’ scores for terms.
Similarly, to use the Loughran and McDonald dictionary:
>>> import pysentiment2 as ps
>>> lm = ps.LM()
>>> tokens = lm.tokenize(text)
>>> score = lm.get_score(tokens)