mirror of
https://github.com/gsi-upm/senpy
synced 2024-11-13 04:02:29 +00:00
277 lines
9.4 KiB
Python
277 lines
9.4 KiB
Python
# -*- coding: utf-8 -*-
|
|
from __future__ import division
|
|
import re
|
|
import nltk
|
|
import os
|
|
import string
|
|
import xml.etree.ElementTree as ET
|
|
from nltk.corpus import stopwords
|
|
from nltk.corpus import WordNetCorpusReader
|
|
from nltk.stem import wordnet
|
|
from emotion import Emotion as Emo
|
|
from senpy.plugins import EmotionPlugin, AnalysisPlugin, ShelfMixin
|
|
from senpy.models import Results, EmotionSet, Entry, Emotion
|
|
|
|
|
|
class WNAffect(EmotionPlugin, ShelfMixin):
|
|
'''
|
|
Emotion classifier using WordNet-Affect to calculate the percentage
|
|
of each emotion. This plugin classifies among 6 emotions: anger,fear,disgust,joy,sadness
|
|
or neutral. The only available language is English (en)
|
|
'''
|
|
name = 'emotion-wnaffect'
|
|
author = ["@icorcuera", "@balkian"]
|
|
version = '0.2'
|
|
extra_params = {
|
|
'language': {
|
|
"@id": 'lang_wnaffect',
|
|
'aliases': ['language', 'l'],
|
|
'required': True,
|
|
'options': ['en',]
|
|
}
|
|
}
|
|
synsets_path = "a-synsets.xml"
|
|
hierarchy_path = "a-hierarchy.xml"
|
|
wn16_path = "wordnet1.6/dict"
|
|
onyx__usesEmotionModel = "emoml:big6"
|
|
nltk_resources = ['stopwords', 'averaged_perceptron_tagger', 'wordnet']
|
|
|
|
def _load_synsets(self, synsets_path):
|
|
"""Returns a dictionary POS tag -> synset offset -> emotion (str -> int -> str)."""
|
|
tree = ET.parse(synsets_path)
|
|
root = tree.getroot()
|
|
pos_map = {"noun": "NN", "adj": "JJ", "verb": "VB", "adv": "RB"}
|
|
|
|
synsets = {}
|
|
for pos in ["noun", "adj", "verb", "adv"]:
|
|
tag = pos_map[pos]
|
|
synsets[tag] = {}
|
|
for elem in root.findall(
|
|
".//{0}-syn-list//{0}-syn".format(pos, pos)):
|
|
offset = int(elem.get("id")[2:])
|
|
if not offset: continue
|
|
if elem.get("categ"):
|
|
synsets[tag][offset] = Emo.emotions[elem.get(
|
|
"categ")] if elem.get(
|
|
"categ") in Emo.emotions else None
|
|
elif elem.get("noun-id"):
|
|
synsets[tag][offset] = synsets[pos_map["noun"]][int(
|
|
elem.get("noun-id")[2:])]
|
|
return synsets
|
|
|
|
def _load_emotions(self, hierarchy_path):
|
|
"""Loads the hierarchy of emotions from the WordNet-Affect xml."""
|
|
|
|
tree = ET.parse(hierarchy_path)
|
|
root = tree.getroot()
|
|
for elem in root.findall("categ"):
|
|
name = elem.get("name")
|
|
if name == "root":
|
|
Emo.emotions["root"] = Emo("root")
|
|
else:
|
|
Emo.emotions[name] = Emo(name, elem.get("isa"))
|
|
|
|
def activate(self, *args, **kwargs):
|
|
|
|
self._stopwords = stopwords.words('english')
|
|
self._wnlemma = wordnet.WordNetLemmatizer()
|
|
self._syntactics = {'N': 'n', 'V': 'v', 'J': 'a', 'S': 's', 'R': 'r'}
|
|
local_path = os.environ.get("SENPY_DATA")
|
|
self._categories = {
|
|
'anger': [
|
|
'general-dislike',
|
|
],
|
|
'fear': [
|
|
'negative-fear',
|
|
],
|
|
'disgust': [
|
|
'shame',
|
|
],
|
|
'joy':
|
|
['gratitude', 'affective', 'enthusiasm', 'love', 'joy', 'liking'],
|
|
'sadness': [
|
|
'ingrattitude', 'daze', 'humility', 'compassion', 'despair',
|
|
'anxiety', 'sadness'
|
|
]
|
|
}
|
|
|
|
self._wnaffect_mappings = {
|
|
'anger': 'anger',
|
|
'fear': 'negative-fear',
|
|
'disgust': 'disgust',
|
|
'joy': 'joy',
|
|
'sadness': 'sadness'
|
|
}
|
|
|
|
self._load_emotions(self.find_file(self.hierarchy_path))
|
|
|
|
if 'total_synsets' not in self.sh:
|
|
total_synsets = self._load_synsets(self.find_file(self.synsets_path))
|
|
self.sh['total_synsets'] = total_synsets
|
|
|
|
self._total_synsets = self.sh['total_synsets']
|
|
|
|
self._wn16_path = self.wn16_path
|
|
self._wn16 = WordNetCorpusReader(self.find_file(self._wn16_path), nltk.data.find(self.find_file(self._wn16_path)))
|
|
|
|
|
|
def deactivate(self, *args, **kwargs):
|
|
self.save()
|
|
|
|
def _my_preprocessor(self, text):
|
|
|
|
regHttp = re.compile(
|
|
'(http://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
|
|
regHttps = re.compile(
|
|
'(https://)[a-zA-Z0-9]*.[a-zA-Z0-9/]*(.[a-zA-Z0-9]*)?')
|
|
regAt = re.compile('@([a-zA-Z0-9]*[*_/&%#@$]*)*[a-zA-Z0-9]*')
|
|
text = re.sub(regHttp, '', text)
|
|
text = re.sub(regAt, '', text)
|
|
text = re.sub('RT : ', '', text)
|
|
text = re.sub(regHttps, '', text)
|
|
text = re.sub('[0-9]', '', text)
|
|
text = self._delete_punctuation(text)
|
|
return text
|
|
|
|
def _delete_punctuation(self, text):
|
|
|
|
exclude = set(string.punctuation)
|
|
s = ''.join(ch for ch in text if ch not in exclude)
|
|
return s
|
|
|
|
def _extract_ngrams(self, text):
|
|
|
|
unigrams_lemmas = []
|
|
pos_tagged = []
|
|
unigrams_words = []
|
|
tokens = text.split()
|
|
for token in nltk.pos_tag(tokens):
|
|
unigrams_words.append(token[0])
|
|
pos_tagged.append(token[1])
|
|
if token[1][0] in self._syntactics.keys():
|
|
unigrams_lemmas.append(
|
|
self._wnlemma.lemmatize(token[0], self._syntactics[token[1]
|
|
[0]]))
|
|
else:
|
|
unigrams_lemmas.append(token[0])
|
|
|
|
return unigrams_words, unigrams_lemmas, pos_tagged
|
|
|
|
def _find_ngrams(self, input_list, n):
|
|
return zip(*[input_list[i:] for i in range(n)])
|
|
|
|
def _clean_pos(self, pos_tagged):
|
|
|
|
pos_tags = {
|
|
'NN': 'NN',
|
|
'NNP': 'NN',
|
|
'NNP-LOC': 'NN',
|
|
'NNS': 'NN',
|
|
'JJ': 'JJ',
|
|
'JJR': 'JJ',
|
|
'JJS': 'JJ',
|
|
'RB': 'RB',
|
|
'RBR': 'RB',
|
|
'RBS': 'RB',
|
|
'VB': 'VB',
|
|
'VBD': 'VB',
|
|
'VGB': 'VB',
|
|
'VBN': 'VB',
|
|
'VBP': 'VB',
|
|
'VBZ': 'VB'
|
|
}
|
|
|
|
for i in range(len(pos_tagged)):
|
|
if pos_tagged[i] in pos_tags:
|
|
pos_tagged[i] = pos_tags[pos_tagged[i]]
|
|
return pos_tagged
|
|
|
|
def _extract_features(self, text):
|
|
|
|
feature_set = {k: 0 for k in self._categories}
|
|
ngrams_words, ngrams_lemmas, pos_tagged = self._extract_ngrams(text)
|
|
matches = 0
|
|
pos_tagged = self._clean_pos(pos_tagged)
|
|
|
|
tag_wn = {
|
|
'NN': self._wn16.NOUN,
|
|
'JJ': self._wn16.ADJ,
|
|
'VB': self._wn16.VERB,
|
|
'RB': self._wn16.ADV
|
|
}
|
|
for i in range(len(pos_tagged)):
|
|
if pos_tagged[i] in tag_wn:
|
|
synsets = self._wn16.synsets(ngrams_words[i],
|
|
tag_wn[pos_tagged[i]])
|
|
if synsets:
|
|
offset = synsets[0].offset()
|
|
if offset in self._total_synsets[pos_tagged[i]]:
|
|
if self._total_synsets[pos_tagged[i]][offset] is None:
|
|
continue
|
|
else:
|
|
emotion = self._total_synsets[pos_tagged[i]][
|
|
offset].get_level(5).name
|
|
matches += 1
|
|
for i in self._categories:
|
|
if emotion in self._categories[i]:
|
|
feature_set[i] += 1
|
|
if matches == 0:
|
|
matches = 1
|
|
|
|
for i in feature_set:
|
|
feature_set[i] = (feature_set[i] / matches)
|
|
|
|
return feature_set
|
|
|
|
def analyse_entry(self, entry, params):
|
|
|
|
text_input = entry['nif:isString']
|
|
|
|
text = self._my_preprocessor(text_input)
|
|
|
|
feature_text = self._extract_features(text)
|
|
|
|
emotionSet = EmotionSet(id="Emotions0")
|
|
emotions = emotionSet.onyx__hasEmotion
|
|
|
|
for i in feature_text:
|
|
emotions.append(
|
|
Emotion(
|
|
onyx__hasEmotionCategory=self._wnaffect_mappings[i],
|
|
onyx__hasEmotionIntensity=feature_text[i]))
|
|
|
|
entry.emotions = [emotionSet]
|
|
|
|
yield entry
|
|
|
|
|
|
def test(self, *args, **kwargs):
|
|
results = list()
|
|
params = {'algo': 'emotion-wnaffect',
|
|
'intype': 'direct',
|
|
'expanded-jsonld': 0,
|
|
'informat': 'text',
|
|
'prefix': '',
|
|
'plugin_type': 'analysisPlugin',
|
|
'urischeme': 'RFC5147String',
|
|
'outformat': 'json-ld',
|
|
'i': 'Hello World',
|
|
'input': 'Hello World',
|
|
'conversion': 'full',
|
|
'language': 'en',
|
|
'algorithm': 'emotion-wnaffect'}
|
|
|
|
self.activate()
|
|
res = next(self.analyse_entry(Entry(nif__isString="This text make me sad"), params))
|
|
texts = {'I hate you': 'anger',
|
|
'i am sad': 'sadness',
|
|
'i am happy with my marks': 'joy',
|
|
'This movie is scary': 'negative-fear'}
|
|
|
|
for text in texts:
|
|
response = next(self.analyse_entry(Entry(nif__isString=text), params))
|
|
expected = texts[text]
|
|
emotionSet = response.emotions[0]
|
|
max_emotion = max(emotionSet['onyx:hasEmotion'], key=lambda x: x['onyx:hasEmotionIntensity'])
|
|
assert max_emotion['onyx:hasEmotionCategory'] == expected
|