pysentiment

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 pysentiment

Usage
`````
To use the Harvard IV-4 dictionary, create an instance of the `HIV4` class

::

    >>> import pysentiment 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 ``pysentiment.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 pysentiment as ps
    >>> lm = ps.LM()
    >>> tokens = lm.tokenize(text)  
    >>> score = lm.get_score(tokens)


Contributions

Bug-fixes / features are always welcome.