[PYTHON] Text sentiment analysis with ML-Ask

Ciao …… †

This time, I would like to introduce Sentiment Analysis Library ML-Ask.

What ML-Ask can do

Emotional estimation

By pattern matching with a 2,100-word dictionary, we estimate 10 types of emotions: {joy, anger, sadness, fear, shame, good, 厭, 昂, cheap, surprise}. These 2,100 words are said to be based on the Emotional Expression Dictionary.

Emotional strength

Emotional strength is estimated by the number of interjections, onomatopoeia, sloppy words, emoticons, and "!" And "?".

Negative / positive classification

Sentences are classified into three types, {negative, positive, and neutral}, based on the estimated emotions.

Contextual considerations

Based on the concept of Contextual Valence Shifters (CVS), we perform emotion estimation considering the context. For example, in the case of the sentence "I don't like it", "like" is denied, so "like" I presume that it is the opposite feeling, "I'm sorry".

Whether it is active

Classify whether the sentence is {ACTIVE, NEUTRAL, PASSIVE} based on the estimated emotion. For example, "昂" is ACTIVE and "sorrow" is PASSIVE.

Python implementation of ML-Ask

It supports both Python 2 and 3 series.

Development repository

https://github.com/ikegami-yukino/pymlask Contributions are welcome!

Installation

pip install pymlask

• It will not work without MeCab

How to use

from mlask import MLAsk
emotion_analyzer = MLAsk()
emotion_analyzer.analyze('I don't hate him!(;´Д`)')
# => {'text': 'I don't hate him!(;´Д`)',
#     'emotion': defaultdict(<class 'list'>,{'yorokobi': ['Hate*CVS'], 'suki': ['Hate*CVS'], 'iya': ['Dislike']}),
#     'orientation': 'mostly_POSITIVE',
#     'activation': 'ACTIVE',
#     'emoticon': ['(;´Д`)'],
#     'intension': 2,
#     'intensifier': {'exclamation': ['！'], 'emotikony': ['(;´Д`)']},
#     'representative': ('yorokobi', ['Hate*CVS'])
#     }