def flatten(self, list):
return wordnet.flatten(list)
def is_emotion(word, shallow=False, pos=None, boolean=True):
""" Guesses whether the word expresses an emotion.
Returns True when the word is an emotion.
When the boolean parameter is set to False,
returns either None or a string hinting at the
emotion the word expresses.
For example:
print is_emotion("blub", pos=wordnet.VERBS, boolean=False)
>>> weep
Preferably the return value would be an is_basic_emotion().
"""
def _return(value):
if boolean and value != None:
return True
elif boolean:
return False
else:
return value
if pos == None \
or pos == wordnet.NOUNS:
ekman = ["anger", "disgust", "fear", "joy", "sadness", "surprise"]
other = ["emotion", "feeling", "expression"]
if pos == wordnet.VERBS:
ekman = ["anger", "disgust", "fear", "enjoy", "sadden", "surprise"]
other = ["empathize", "feel", "express emotion", "express"]
if pos == wordnet.ADJECTIVES \
or pos == wordnet.ADVERBS:
ekman = ["angry", "disgusted", "fearful", "happy", "sad", "surprised"]
other = ["emotional"]
word = word.lower().strip()
# Check the naive lists first.
for i in range(len(commonsense_naive_ekman)):
if word in commonsense_naive_ekman[i]:
return _return(commonsense_ekman[i])
# Fair competition:
# if we shuffle the list we have an equal speed
# for each Ekman emotion to scan.
from random import shuffle
indices = range(len(ekman))
shuffle(indices)
# For each Ekman emotion,
# take all of its senses,
# and check the hyponyms of that sense.
for i in indices:
emotion = ekman[i]
s = wordnet.senses(emotion, pos)
for j in range(len(s)):
if word in s[j]:
return _return(commonsense_ekman[i])
h = wordnet.hyponyms(emotion, j, pos)
h = wordnet.flatten(h)
if word in h:
return _return(commonsense_ekman[i])
# Maybe we get lucky and WordNet has tagged
# the word as a feeling.
if shallow and wordnet.lexname(word, 0, pos) == "feeling":
return _return("feeling")
# Take a generalised word like "emotion"
# and traverse its hyponyms.
# When performing a deep search,
# traverse the hyponyms of those hyponyms as well.
# Example: "yearning" -> "desire" -> "feeling"
for emotion in other:
for w in wordnet.flatten(wordnet.hyponyms(emotion, 0, pos)):
if word == w:
return _return(emotion)
if not shallow:
if word in wordnet.flatten(wordnet.hyponym(w, 0, pos)):
return _return(w)
return _return(None)
def is_emotion(word, shallow=False, pos=None, boolean=True):
""" Guesses whether the word expresses an emotion.
Returns True when the word is an emotion.
When the boolean parameter is set to False,
returns either None or a string hinting at the
emotion the word expresses.
For example:
print is_emotion("blub", pos=wordnet.VERBS, boolean=False)
>>> weep
Preferably the return value would be an is_basic_emotion().
"""
def _return(value):
if boolean and value != None:
return True
elif boolean:
return False
else:
return value
if pos == None \
or pos == wordnet.NOUNS:
ekman = ["anger", "disgust", "fear", "joy", "sadness", "surprise"]
other = ["emotion", "feeling", "expression"]
if pos == wordnet.VERBS:
ekman = ["anger", "disgust", "fear", "enjoy", "sadden", "surprise"]
other = ["empathize", "feel", "express emotion", "express"]
if pos == wordnet.ADJECTIVES \
or pos == wordnet.ADVERBS:
ekman = ["angry", "disgusted", "fearful", "happy", "sad", "surprised"]
other = ["emotional"]
word = word.lower().strip()
# Check the naive lists first.
for i in range(len(commonsense_naive_ekman)):
if word in commonsense_naive_ekman[i]:
return _return(commonsense_ekman[i])
# Fair competition:
# if we shuffle the list we have an equal speed
# for each Ekman emotion to scan.
from random import shuffle
indices = range(len(ekman))
shuffle(indices)
# For each Ekman emotion,
# take all of its senses,
# and check the hyponyms of that sense.
for i in indices:
emotion = ekman[i]
s = wordnet.senses(emotion, pos)
for j in range(len(s)):
if word in s[j]:
return _return(commonsense_ekman[i])
h = wordnet.hyponyms(emotion, j, pos)
h = wordnet.flatten(h)
if word in h:
return _return(commonsense_ekman[i])
# Maybe we get lucky and WordNet has tagged
# the word as a feeling.
if shallow and wordnet.lexname(word, 0, pos) == "feeling":
return _return("feeling")
# Take a generalised word like "emotion"
# and traverse its hyponyms.
# When performing a deep search,
# traverse the hyponyms of those hyponyms as well.
# Example: "yearning" -> "desire" -> "feeling"
for emotion in other:
for w in wordnet.flatten(wordnet.hyponyms(emotion, 0, pos)):
if word == w:
return _return(emotion)
if not shallow:
if word in wordnet.flatten(wordnet.hyponym(w, 0, pos)):
return _return(w)
return _return(None)
