stormkit

This is a collection of tools created by me to help with my own artwork. It currently includes multiple-mood sentiment analysis (based on the bag of words model). Soon to include other tools including point of speech tagging.

How to install

Easiest to install via pip:

pip install stormkit

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Multiple Mood Sentiment Analaysis

This class can be used to determine the sentiment in text, for example the percentage of anger, sadness. It currently supports, positivity, negativity, anger, worry, sadness and scare.

Import the library

from stormkit.sentimentAnalysis import *

To initiate the class call

sentiment = MultiMoodSentimentAnalysis()

To analyse a string call the function analyse_text(), this will return a 'SentimentResult' object

result = sen.analyse_text("I hate Trump, I'm furious, but I love ice cream.")

To check if any errors occurred simply check the error variable, this should be done whenever creating a new 'SentimentResult' object

if result.error is None:
    print(result.decimals)
else:
    print(result.error)

To get the results of the sentiment analysis use one of the following

# this is the raw data, including everything from words matched, 
# occurrences and different types of mood. Good for debugging
result.raw

# this returns an array containing the string of the dominant 
# emotion(s) within the text, note - this can be more than one
result.majority_emotion

# this returns the decimals of emotions within the text 
# (in a dictionary), this is a float within the value of 0.0 
# and 1.0, the emotion name is the key
result.decimals

# this returns an array containing the words that were matched for
# the specified emotion, for example ["sad", "upset"]
result.words_for_emotion("angry")

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Atrributions

The two word list files 'neg-lex.txt' and 'pos-lex.txt' are from http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html and are based on the following papers:

Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, Washington, USA, http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html

Bing Liu, Minqing Hu and Junsheng Cheng. "Opinion Observer: Analyzing and Comparing Opinions on the Web." Proceedings of the 14th International World Wide Web conference (WWW-2005), May 10-14, 2005, Chiba, Japan.

The word list files 'angry-lex.txt', 'sad-lex.txt', 'worry-lex.txt' and 'scared-lex.txt' were created by me.