"""Sentiment analysis implementations.
.. versionadded:: 0.5.0
"""
from collections import namedtuple
import nltk
from textblob.base import CONTINUOUS, DISCRETE, BaseSentimentAnalyzer
from textblob.decorators import requires_nltk_corpus
from textblob.en import sentiment as pattern_sentiment
from textblob.tokenizers import word_tokenize
[docs]
class PatternAnalyzer(BaseSentimentAnalyzer):
"""Sentiment analyzer that uses the same implementation as the
pattern library. Returns results as a named tuple of the form:
``Sentiment(polarity, subjectivity, [assessments])``
where [assessments] is a list of the assessed tokens and their
polarity and subjectivity scores
"""
kind = CONTINUOUS
# This is only here for backwards-compatibility.
# The return type is actually determined upon calling analyze()
RETURN_TYPE = namedtuple("Sentiment", ["polarity", "subjectivity"])
[docs]
def analyze(self, text, keep_assessments=False):
"""Return the sentiment as a named tuple of the form:
``Sentiment(polarity, subjectivity, [assessments])``.
"""
#: Return type declaration
if keep_assessments:
Sentiment = namedtuple(
"Sentiment", ["polarity", "subjectivity", "assessments"]
)
assessments = pattern_sentiment(text).assessments
polarity, subjectivity = pattern_sentiment(text)
return Sentiment(polarity, subjectivity, assessments)
else:
Sentiment = namedtuple("Sentiment", ["polarity", "subjectivity"])
return Sentiment(*pattern_sentiment(text))
def _default_feature_extractor(words):
"""Default feature extractor for the NaiveBayesAnalyzer."""
return dict((word, True) for word in words)
[docs]
class NaiveBayesAnalyzer(BaseSentimentAnalyzer):
"""Naive Bayes analyzer that is trained on a dataset of movie reviews.
Returns results as a named tuple of the form:
``Sentiment(classification, p_pos, p_neg)``
:param callable feature_extractor: Function that returns a dictionary of
features, given a list of words.
"""
kind = DISCRETE
#: Return type declaration
RETURN_TYPE = namedtuple("Sentiment", ["classification", "p_pos", "p_neg"])
def __init__(self, feature_extractor=_default_feature_extractor):
super().__init__()
self._classifier = None
self.feature_extractor = feature_extractor
[docs]
@requires_nltk_corpus
def train(self):
"""Train the Naive Bayes classifier on the movie review corpus."""
super().train()
neg_ids = nltk.corpus.movie_reviews.fileids("neg")
pos_ids = nltk.corpus.movie_reviews.fileids("pos")
neg_feats = [
(
self.feature_extractor(nltk.corpus.movie_reviews.words(fileids=[f])),
"neg",
)
for f in neg_ids
]
pos_feats = [
(
self.feature_extractor(nltk.corpus.movie_reviews.words(fileids=[f])),
"pos",
)
for f in pos_ids
]
train_data = neg_feats + pos_feats
self._classifier = nltk.classify.NaiveBayesClassifier.train(train_data)
[docs]
def analyze(self, text):
"""Return the sentiment as a named tuple of the form:
``Sentiment(classification, p_pos, p_neg)``
"""
# Lazily train the classifier
super().analyze(text)
tokens = word_tokenize(text, include_punc=False)
filtered = (t.lower() for t in tokens if len(t) >= 3)
feats = self.feature_extractor(filtered)
prob_dist = self._classifier.prob_classify(feats)
return self.RETURN_TYPE(
classification=prob_dist.max(),
p_pos=prob_dist.prob("pos"),
p_neg=prob_dist.prob("neg"),
)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4