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95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
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# coding: utf-8
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# In[1]:
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# -*- coding: utf-8 -*-
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"""
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Clement Michard (c) 2015
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"""
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import os
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import sys
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import nltk
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from emotion import Emotion
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from nltk.corpus import WordNetCorpusReader
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import xml.etree.ElementTree as ET
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class WNAffect:
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"""WordNet-Affect resource."""
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nltk_resources = ['averaged_perceptron_tagger']
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def __init__(self, wordnet16_dir, wn_domains_dir):
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"""Initializes the WordNet-Affect object."""
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cwd = os.getcwd()
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nltk.data.path.append(cwd)
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wn16_path = "{0}/dict".format(wordnet16_dir)
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self.wn16 = WordNetCorpusReader(os.path.abspath("{0}/{1}".format(cwd, wn16_path)), nltk.data.find(wn16_path))
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self.flat_pos = {'NN':'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'}
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self.wn_pos = {'NN':self.wn16.NOUN, 'JJ':self.wn16.ADJ, 'VB':self.wn16.VERB, 'RB':self.wn16.ADV}
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self._load_emotions(wn_domains_dir)
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self.synsets = self._load_synsets(wn_domains_dir)
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def _load_synsets(self, wn_domains_dir):
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"""Returns a dictionary POS tag -> synset offset -> emotion (str -> int -> str)."""
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tree = ET.parse("{0}/a-synsets.xml".format(wn_domains_dir))
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root = tree.getroot()
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pos_map = { "noun": "NN", "adj": "JJ", "verb": "VB", "adv": "RB" }
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synsets = {}
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for pos in ["noun", "adj", "verb", "adv"]:
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tag = pos_map[pos]
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synsets[tag] = {}
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for elem in root.findall(".//{0}-syn-list//{0}-syn".format(pos, pos)):
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offset = int(elem.get("id")[2:])
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if not offset: continue
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if elem.get("categ"):
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synsets[tag][offset] = Emotion.emotions[elem.get("categ")] if elem.get("categ") in Emotion.emotions else None
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elif elem.get("noun-id"):
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synsets[tag][offset] = synsets[pos_map["noun"]][int(elem.get("noun-id")[2:])]
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return synsets
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def _load_emotions(self, wn_domains_dir):
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"""Loads the hierarchy of emotions from the WordNet-Affect xml."""
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tree = ET.parse("{0}/a-hierarchy.xml".format(wn_domains_dir))
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root = tree.getroot()
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for elem in root.findall("categ"):
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name = elem.get("name")
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if name == "root":
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Emotion.emotions["root"] = Emotion("root")
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else:
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Emotion.emotions[name] = Emotion(name, elem.get("isa"))
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def get_emotion(self, word, pos):
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"""Returns the emotion of the word.
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word -- the word (str)
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pos -- part-of-speech (str)
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"""
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if pos in self.flat_pos:
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pos = self.flat_pos[pos]
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synsets = self.wn16.synsets(word, self.wn_pos[pos])
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if synsets:
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offset = synsets[0].offset()
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if offset in self.synsets[pos]:
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return self.synsets[pos][offset]
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return None
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if __name__ == "__main__":
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wordnet16, wndomains32, word, pos = sys.argv[1:5]
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wna = WNAffect(wordnet16, wndomains32)
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print wna.get_emotion(word, pos)
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