Simple sentiment analysis with Ruby
Sentences are tokenized and tokens are assigned a numerical score for their average sentiment. The total score is then used to determine the overall sentiment in relation to the threshold.
For example, the default threshold is 0.0. If a sentence has a score of 0, it is deemed "neutral". Higher than the thresold is "positive", lower is "negative".
If you set the threshold to a non-zero amount, e.g. 0.25:
- Positive scores are > 0.25
- Neutral scores are -0.25 - 0.25
- Negative scores are < -0.25
# Create an instance for usage
analyzer = Sentimental.new
# Load the default sentiment dictionaries
analyzer.load_defaults
# And/or load your own dictionaries
analyzer.load_senti_file('path/to/your/file.txt')
# Set a global threshold
analyzer.threshold = 0.1
# Use your analyzer
analyzer.sentiment 'I love ruby'
#=> :positive
analyzer.sentiment 'I like ruby'
#=> :neutral
analyzer.sentiment 'I really like ruby'
#=> :positive
# You can make new analyzers with individual thresholds:
analyzer = Sentimental.new(threshold: 0.9)
analyzer.sentiment 'I love ruby'
#=> :positive
analyzer.sentiment 'I like ruby'
#=> :neutral
analyzer.sentiment 'I really like ruby'
#=> :neutral
# Get the numerical score of a string:
analyzer.score 'I love ruby'
#=> 0.925These are currently plain-text files containing whitespace-separated scores and tokens, e.g.:
1.0 Awesome
0.0 Meh
-1.0 Horrible
You can parse n-grams of words by specifying their max size in the initializer:
Sentimental.new(ngrams: 4)
The dictionary must have this format:
1.0 very happy
-2.0 no
0.0 meh
gem install sentimental
MIT License
Based largely on Christopher MacLellan's script: https://github.com/cmaclell/Basic-Tweet-Sentiment-Analyzer
- 2013-10-13 Adding :-) to slang