@ -1,49 +1,56 @@
#!/usr/local/bin/python
# coding: utf-8
from future import standard_library
standard_library . install_aliases ( )
import os
import re
import sys
import string
import numpy as np
import pandas as pd
from six . moves import urllib
from nltk . corpus import stopwords
from senpy import Emotion Plugin, Text Box, models
from senpy import Emotion Box, models
def ignore ( dchars ) :
deletechars = " " . join ( dchars )
if sys . version_info [ 0 ] > = 3 :
tbl = str . maketrans ( " " , " " , deletechars )
ignore = lambda s : s . translate ( tbl )
else :
def ignore ( s ) :
return string . translate ( s , None , deletechars )
tbl = str . maketrans ( " " , " " , deletechars )
ignore = lambda s : s . translate ( tbl )
return ignore
class DepecheMood ( TextBox , EmotionPlugin ) :
''' Plugin that uses the DepecheMood++ emotion lexicon. '''
class DepecheMood ( EmotionBox ) :
'''
Plugin that uses the DepecheMood emotion lexicon .
DepecheMood is an emotion lexicon automatically generated from news articles where users expressed their associated emotions . It contains two languages ( English and Italian ) , as well as three types of word representations ( token , lemma and lemma #PoS). For English, the lexicon contains 165k tokens, while the Italian version contains 116k. Unsupervised techniques can be applied to generate simple but effective baselines. To learn more, please visit https://github.com/marcoguerini/DepecheMood and http://www.depechemood.eu/
'''
author = ' Oscar Araque '
name = ' emotion-depechemood '
version = ' 0.1 '
requirements = [ ' pandas ' ]
nltk_resources = [ " stopwords " ]
onyx__usesEmotionModel = ' wna:WNAModel '
EMOTIONS = [ ' wna:negative-fear ' ,
' wna:amusement ' ,
' wna:anger ' ,
' wna:annoyance ' ,
' wna:indifference ' ,
' wna:joy ' ,
' wna:awe ' ,
' wna:sadness ' ]
DM_EMOTIONS = [ ' AFRAID ' , ' AMUSED ' , ' ANGRY ' , ' ANNOYED ' , ' DONT_CARE ' , ' HAPPY ' , ' INSPIRED ' , ' SAD ' , ]
def __init__ ( self , * args , * * kwargs ) :
super ( DepecheMood , self ) . __init__ ( * args , * * kwargs )
self . LEXICON_URL = " https://github.com/marcoguerini/DepecheMood/raw/master/DepecheMood % 2B % 2B/DepecheMood_english_token_full.tsv "
self . EMOTIONS = [ ' AFRAID ' , ' AMUSED ' , ' ANGRY ' , ' ANNOYED ' , ' DONT_CARE ' , ' HAPPY ' , ' INSPIRED ' , ' SAD ' , ]
self . _mapping = {
' AFRAID ' : ' wna:negative-fear ' ,
' AMUSED ' : ' wna:amusement ' ,
' ANGRY ' : ' wna:anger ' ,
' ANNOYED ' : ' wna:annoyance ' ,
' DONT_CARE ' : ' wna:indifference ' ,
' HAPPY ' : ' wna:joy ' ,
' INSPIRED ' : ' wna:awe ' ,
' SAD ' : ' wna:sadness ' ,
}
self . _denoise = ignore ( set ( string . punctuation ) | set ( ' «» ' ) )
self . _stop_words = [ ]
self . _lex_vocab = None
@ -89,19 +96,21 @@ class DepecheMood(TextBox, EmotionPlugin):
return S
def estimate_all_emotions ( self , tokens ) :
S = {}
S = []
intersection = set ( tokens ) & self . _lex_vocab
for emotion in self . :
for emotion in self . DM_ EMOTIONS:
s = self . estimate_emotion ( intersection , emotion )
emotion_mapped = self . _mapping [ emotion ]
S [ emotion_mapped ] = s
S . append ( s )
return S
def download_lex ( self , file_path = ' DepecheMood_english_token_full.tsv ' , freq_threshold = 10 ) :
import pandas as pd
try :
file_path = self . find_file ( file_path )
except IOError :
file_path = self . path ( file_path )
filename , _ = urllib . request . urlretrieve ( self . LEXICON_URL , file_path )
lexicon = pd . read_csv ( file_path , sep = ' \t ' , index_col = 0 )
@ -110,18 +119,8 @@ class DepecheMood(TextBox, EmotionPlugin):
lexicon = lexicon . T . to_dict ( )
return lexicon
def output ( self , output , entry , * * kwargs ) :
s = models . EmotionSet ( )
s . prov__wasGeneratedBy = self . id
entry . emotions . append ( s )
for label , value in output . items ( ) :
e = models . Emotion ( onyx__hasEmotionCategory = label ,
onyx__hasEmotionIntensity = value )
s . onyx__hasEmotion . append ( e )
return entry
def predict_one ( self , input , * * kwargs ) :
tokens = self . preprocess ( input )
def predict_one ( self , features , * * kwargs ) :
tokens = self . preprocess ( features [ 0 ] )
estimation = self . estimate_all_emotions ( tokens )
return estimation
@ -131,26 +130,41 @@ class DepecheMood(TextBox, EmotionPlugin):
' nif:isString ' : ' My cat is very happy ' ,
} ,
' expected ' : {
' emotions ' : [
' onyx:hasEmotionSet ' : [
{
' @type ' : ' emotionSet ' ,
' onyx:hasEmotion ' : [
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:negative-fear ' ,
' onyx:hasEmotionIntensity ' : 0.05278117640010922 , } ,
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:amusement ' ,
' onyx:hasEmotionIntensity ' : 0.2114806151413433 , } ,
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:anger ' ,
' onyx:hasEmotionIntensity ' : 0.05726119426520887 , } ,
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:annoyance ' ,
' onyx:hasEmotionIntensity ' : 0.12295990731053638 , } ,
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:indifference ' ,
' onyx:hasEmotionIntensity ' : 0.1860159893608025 , } ,
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:joy ' ,
' onyx:hasEmotionIntensity ' : 0.12904050973724163 , } ,
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:awe ' ,
' onyx:hasEmotionIntensity ' : 0.17973650399862967 , } ,
{ ' @type ' : ' emotion ' , ' onyx:hasEmotionCategory ' : ' wna:sadness ' ,
' onyx:hasEmotionIntensity ' : 0.060724103786128455 , } ,
{
' onyx:hasEmotionCategory ' : ' wna:negative-fear ' ,
' onyx:hasEmotionIntensity ' : 0.05278117640010922
} ,
{
' onyx:hasEmotionCategory ' : ' wna:amusement ' ,
' onyx:hasEmotionIntensity ' : 0.2114806151413433 ,
} ,
{
' onyx:hasEmotionCategory ' : ' wna:anger ' ,
' onyx:hasEmotionIntensity ' : 0.05726119426520887
} ,
{
' onyx:hasEmotionCategory ' : ' wna:annoyance ' ,
' onyx:hasEmotionIntensity ' : 0.12295990731053638 ,
} ,
{
' onyx:hasEmotionCategory ' : ' wna:indifference ' ,
' onyx:hasEmotionIntensity ' : 0.1860159893608025 ,
} ,
{
' onyx:hasEmotionCategory ' : ' wna:joy ' ,
' onyx:hasEmotionIntensity ' : 0.12904050973724163 ,
} ,
{
' onyx:hasEmotionCategory ' : ' wna:awe ' ,
' onyx:hasEmotionIntensity ' : 0.17973650399862967 ,
} ,
{
' onyx:hasEmotionCategory ' : ' wna:sadness ' ,
' onyx:hasEmotionIntensity ' : 0.060724103786128455 ,
} ,
]
}
]
@ -164,4 +178,4 @@ if __name__ == '__main__':
# sp, app = easy_load()
# for plug in sp.analysis_plugins:
# plug.test()
easy ( )
easy _test ( debug = False )
J. Fernando Sánchez
5 years ago