pysent3 0.1.2

Sentiment Analysis in Python using a Dictionary Approach

pysentiment

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/

Getting Started

pip install pysent3

import pysent3

Usage

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

import pysent3 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 pysentiment3.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 pysent3 as ps
lm = ps.LM()
tokens = lm.tokenize(text)
score = lm.get_score(tokens)

Largely based on pysentiment2 created by Nick DeRobertis and based on pysentiment by Zhichao Han. GNU GPL License.