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.
>>> 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")