def load_riegel15():
riegel15 = pd.read_excel(cs.riegel15)
riegel15 = riegel15[['NAWL_word', 'val_M_all', 'aro_M_all']]
riegel15.columns = ['Word', 'Valence', 'Arousal']
riegel15['Valence'] = scaleInRange(riegel15['Valence'],
oldmin=-3,
oldmax=3,
newmin=1,
newmax=9)
riegel15['Arousal'] = scaleInRange(riegel15['Arousal'],
oldmin=1,
oldmax=5,
newmin=1,
newmax=9)
riegel15.set_index('Word', inplace=True)
return riegel15
def load_schmidtke14(lower_case=False):
schmidtke14 = pd.read_excel(cs.schmidtke14)
# schmidtke14=schmidtke14[['Word','Valence','Arousal','Dominance']]
schmidtke14 = schmidtke14[[
'G-word', 'VAL_Mean', 'ARO_Mean_(ANEW)', 'DOM_Mean'
]]
schmidtke14.columns = ['Word', 'Valence', 'Arousal', 'Dominance']
# schmidtke14['Word']=schmidtke14['Word'].str.lower()
schmidtke14.set_index('Word', inplace=True)
if lower_case:
schmidtke14.index = schmidtke14.index.str.lower()
#schmidtke14=schmidtke14[~schmidtke14.index.duplicated(keep='first')]
schmidtke14 = drop_duplicates(schmidtke14)
schmidtke14.Valence = [
scaleInRange(x=x, oldmin=-3., oldmax=3., newmin=1., newmax=9.)
for x in schmidtke14.Valence
]
# ### setting word column to lower case for compatiblity with briesemeister11
# # print(schmidtke14.head())
# # print(schmidtke14.shape)
return schmidtke14
def load_vo09():
df = pd.read_csv(cs.vo09, sep=';')
df = df[['WORD_LOWER', 'EMO_MEAN', 'AROUSAL_MEAN']]
df.columns = ['Word', 'Valence', 'Arousal']
df.set_index('Word', inplace=True)
# usecols='WORD_LOWER', 'EMO_MEAN','AROUSAL_MEAN', '')
df['Valence'] = scaleInRange(x=df['Valence'],
oldmin=-3,
oldmax=3,
newmin=1,
newmax=9)
df['Arousal'] = scaleInRange(x=df['Arousal'],
oldmin=1,
oldmax=5,
newmin=1,
newmax=9)
return df
def load_palogiannidi16():
df = pd.read_csv(cs.palogiannidi16)
for var in ['Valence', 'Arousal', 'Dominance']:
df[var] = scaleInRange(x=df[var],
oldmin=-1,
oldmax=1,
newmin=1,
newmax=9)
df = df[['Greek word', 'Valence', 'Arousal', 'Dominance']]
df.columns = heads_vad
df.set_index('Word', inplace=True)
# print(df)
# print(df.min(axis=0))
# print(df.max(axis=0))
return df
def load_eilola10():
'''
Eilola, T. M., & Havelka, J. (2010). Affective norms for 210 British
English and Finnish nouns. Behavior Research Methods, 42(1), 134–140.
'''
eilola10 = pd.read_excel(cs.eilola10)
eilola10 = eilola10[[
'Finnish Word', 'Finnish Valence mean', 'Finnish Emotional Charge mean'
]]
eilola10.columns = ['Word', 'Valence', 'Arousal']
eilola10.set_index('Word', inplace=True)
for v in eilola10.columns:
eilola10[v] = scaleInRange(eilola10[v],
oldmin=0,
oldmax=9,
newmin=1,
newmax=9)
return eilola10
def load_wierzba15():
wierzba15 = pd.read_excel(cs.wierzba15)
wierzba15 = wierzba15[[
'NAWL_word', 'hap_M_all', 'ang_M_all', 'sad_M_all', 'fea_M_all',
'dis_M_all'
]]
wierzba15.columns = heads_be5
wierzba15.set_index('Word', inplace=True)
## rescaling basic emotions
## Scaling
for cat in ['Joy', 'Anger', 'Sadness', 'Fear', 'Disgust']:
wierzba15[cat] = [
scaleInRange(x=x, oldmin=1., oldmax=7., newmin=1., newmax=5.)
for x in wierzba15[cat]
]
# print(wierzba15.head())
# print(wierzba15.shape)
return wierzba15