def __init__(self,
use_lemmas=False,
entity_types_to_censor=set(),
tag_types_to_censor=set(),
strip_final_period=False,
empath_analyze_function=None,
**kwargs):
'''
Parameters
----------
empath_analyze_function: function (default=empath.Empath().analyze)
Function that produces a dictionary mapping Empath categories to
Other parameters from FeatsFromSpacyDoc.__init__
'''
if empath_analyze_function is None:
try:
import empath
except ImportError:
raise Exception(
"Please install the empath library to use FeatsFromSpacyDocAndEmpath."
)
self._empath_analyze_function = empath.Empath().analyze
else:
self._empath_analyze_function = partial(
empath_analyze_function, kwargs={'tokenizer': 'bigram'})
super(FeatsFromSpacyDocAndEmpath,
self).__init__(use_lemmas, entity_types_to_censor,
tag_types_to_censor, strip_final_period)
def get_top_model_term_lists(self):
try:
import empath
except ImportError:
raise Exception(
"Please install the empath library to use FeatsFromSpacyDocAndEmpath."
)
return dict(empath.Empath().cats)
def __init__(self, topic_keywords):
self.lexicon = empath.Empath()
self.keyword_analysis = []
self.topic_keywords = topic_keywords
self.topics_set = None