Wordlist-based sentiment analysis for Elm
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elm-sentiment is an Elm module that uses the AFINN-111 wordlist to perform sentiment analysis on arbitrary blocks of input text. Other wordlists are easy to integrate.

It is inspired by the the Sentiment-module for Node.js.

Please note that a wordlist-based approach for sentiment analysis might not be the best available approach for every (your) application. It is a simple and easy to use solution that does not need training like a Bayes classifier, that might perform better in classifying sentiments.


elm package install ggb/elm-sentiment


Usage is straightforward:

import Sentiment

tweet = """
#StarWars fans are the best kind of people. 
I'm so, so lucky & honored to get to hang 
out with you at Celebration. Thank you for 
being you.

Sentiment.analyse tweet

-- Result:
-- { tokens = ["starwars","fans","are","the","best", ... ,"for","being","you"]
-- , score = 12
-- , words = ["best","kind","lucky","honored","thank"]
-- , positive = [3,2,3,2,2]
-- , negative = []
-- , comparative = 0.42857142857142855 
-- }

For more advanced usage please take a look at the function-level documentation and especially at the analyseWith-function.


There are lots of possibilities to improve the current module. Some ideas:

  • handling of negations
  • more and different word lists
  • compression of word lists
  • possibility to train a model (word list as fallback or support)