The sentiment module provides methods for returning word-level and average word sentiment scores, currently for English only.

The sentiment data is from the article Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, and Christopher M. Danforth

Sentiments range from -1.0 to 1.0, where -1.0 is the most unfavorable, and 1.0 is the most favorable.

In addition, a Sentiment object can be inspected for the original values from the Hedonometrics paper.

Examples::

>>> from sentimenicon import sentiment
>>> a = sentiment.Analyzer
>>> print a.word_sentiment("happy")
>>> print a.word_sentiment("terrorist")
>>> print a.average_word_sentiment("I love a happy friend".lower().split(" "))
>>> s = a.sentiment_object("happy")