230 lines
9.6 KiB
Python
230 lines
9.6 KiB
Python
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# coding: utf-8
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#
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# Natural Language Toolkit: Sentiment Analyzer
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Pierpaolo Pantone <24alsecondo@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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A SentimentAnalyzer is a tool to implement and facilitate Sentiment Analysis tasks
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using NLTK features and classifiers, especially for teaching and demonstrative
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purposes.
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"""
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from __future__ import print_function
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from collections import defaultdict
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from nltk.classify.util import apply_features, accuracy as eval_accuracy
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from nltk.collocations import BigramCollocationFinder
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from nltk.metrics import (BigramAssocMeasures, precision as eval_precision,
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recall as eval_recall, f_measure as eval_f_measure)
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from nltk.probability import FreqDist
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from nltk.sentiment.util import save_file, timer
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class SentimentAnalyzer(object):
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"""
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A Sentiment Analysis tool based on machine learning approaches.
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"""
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def __init__(self, classifier=None):
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self.feat_extractors = defaultdict(list)
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self.classifier = classifier
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def all_words(self, documents, labeled=None):
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"""
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Return all words/tokens from the documents (with duplicates).
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:param documents: a list of (words, label) tuples.
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:param labeled: if `True`, assume that each document is represented by a
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(words, label) tuple: (list(str), str). If `False`, each document is
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considered as being a simple list of strings: list(str).
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:rtype: list(str)
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:return: A list of all words/tokens in `documents`.
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"""
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all_words = []
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if labeled is None:
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labeled = documents and isinstance(documents[0], tuple)
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if labeled == True:
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for words, sentiment in documents:
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all_words.extend(words)
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elif labeled == False:
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for words in documents:
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all_words.extend(words)
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return all_words
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def apply_features(self, documents, labeled=None):
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"""
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Apply all feature extractor functions to the documents. This is a wrapper
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around `nltk.classify.util.apply_features`.
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If `labeled=False`, return featuresets as:
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[feature_func(doc) for doc in documents]
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If `labeled=True`, return featuresets as:
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[(feature_func(tok), label) for (tok, label) in toks]
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:param documents: a list of documents. `If labeled=True`, the method expects
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a list of (words, label) tuples.
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:rtype: LazyMap
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"""
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return apply_features(self.extract_features, documents, labeled)
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def unigram_word_feats(self, words, top_n=None, min_freq=0):
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"""
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Return most common top_n word features.
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:param words: a list of words/tokens.
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:param top_n: number of best words/tokens to use, sorted by frequency.
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:rtype: list(str)
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:return: A list of `top_n` words/tokens (with no duplicates) sorted by
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frequency.
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"""
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# Stopwords are not removed
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unigram_feats_freqs = FreqDist(word for word in words)
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return [w for w, f in unigram_feats_freqs.most_common(top_n)
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if unigram_feats_freqs[w] > min_freq]
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def bigram_collocation_feats(self, documents, top_n=None, min_freq=3,
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assoc_measure=BigramAssocMeasures.pmi):
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"""
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Return `top_n` bigram features (using `assoc_measure`).
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Note that this method is based on bigram collocations measures, and not
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on simple bigram frequency.
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:param documents: a list (or iterable) of tokens.
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:param top_n: number of best words/tokens to use, sorted by association
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measure.
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:param assoc_measure: bigram association measure to use as score function.
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:param min_freq: the minimum number of occurrencies of bigrams to take
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into consideration.
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:return: `top_n` ngrams scored by the given association measure.
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"""
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finder = BigramCollocationFinder.from_documents(documents)
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finder.apply_freq_filter(min_freq)
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return finder.nbest(assoc_measure, top_n)
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def classify(self, instance):
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"""
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Classify a single instance applying the features that have already been
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stored in the SentimentAnalyzer.
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:param instance: a list (or iterable) of tokens.
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:return: the classification result given by applying the classifier.
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"""
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instance_feats = self.apply_features([instance], labeled=False)
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return self.classifier.classify(instance_feats[0])
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def add_feat_extractor(self, function, **kwargs):
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"""
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Add a new function to extract features from a document. This function will
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be used in extract_features().
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Important: in this step our kwargs are only representing additional parameters,
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and NOT the document we have to parse. The document will always be the first
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parameter in the parameter list, and it will be added in the extract_features()
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function.
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:param function: the extractor function to add to the list of feature extractors.
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:param kwargs: additional parameters required by the `function` function.
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"""
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self.feat_extractors[function].append(kwargs)
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def extract_features(self, document):
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"""
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Apply extractor functions (and their parameters) to the present document.
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We pass `document` as the first parameter of the extractor functions.
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If we want to use the same extractor function multiple times, we have to
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add it to the extractors with `add_feat_extractor` using multiple sets of
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parameters (one for each call of the extractor function).
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:param document: the document that will be passed as argument to the
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feature extractor functions.
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:return: A dictionary of populated features extracted from the document.
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:rtype: dict
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"""
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all_features = {}
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for extractor in self.feat_extractors:
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for param_set in self.feat_extractors[extractor]:
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feats = extractor(document, **param_set)
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all_features.update(feats)
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return all_features
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def train(self, trainer, training_set, save_classifier=None, **kwargs):
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"""
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Train classifier on the training set, optionally saving the output in the
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file specified by `save_classifier`.
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Additional arguments depend on the specific trainer used. For example,
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a MaxentClassifier can use `max_iter` parameter to specify the number
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of iterations, while a NaiveBayesClassifier cannot.
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:param trainer: `train` method of a classifier.
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E.g.: NaiveBayesClassifier.train
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:param training_set: the training set to be passed as argument to the
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classifier `train` method.
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:param save_classifier: the filename of the file where the classifier
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will be stored (optional).
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:param kwargs: additional parameters that will be passed as arguments to
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the classifier `train` function.
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:return: A classifier instance trained on the training set.
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:rtype:
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"""
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print("Training classifier")
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self.classifier = trainer(training_set, **kwargs)
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if save_classifier:
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save_file(self.classifier, save_classifier)
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return self.classifier
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def evaluate(self, test_set, classifier=None, accuracy=True, f_measure=True,
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precision=True, recall=True, verbose=False):
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"""
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Evaluate and print classifier performance on the test set.
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:param test_set: A list of (tokens, label) tuples to use as gold set.
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:param classifier: a classifier instance (previously trained).
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:param accuracy: if `True`, evaluate classifier accuracy.
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:param f_measure: if `True`, evaluate classifier f_measure.
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:param precision: if `True`, evaluate classifier precision.
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:param recall: if `True`, evaluate classifier recall.
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:return: evaluation results.
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:rtype: dict(str): float
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"""
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if classifier is None:
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classifier = self.classifier
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print("Evaluating {0} results...".format(type(classifier).__name__))
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metrics_results = {}
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if accuracy == True:
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accuracy_score = eval_accuracy(classifier, test_set)
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metrics_results['Accuracy'] = accuracy_score
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gold_results = defaultdict(set)
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test_results = defaultdict(set)
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labels = set()
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for i, (feats, label) in enumerate(test_set):
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labels.add(label)
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gold_results[label].add(i)
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observed = classifier.classify(feats)
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test_results[observed].add(i)
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for label in labels:
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if precision == True:
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precision_score = eval_precision(gold_results[label],
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test_results[label])
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metrics_results['Precision [{0}]'.format(label)] = precision_score
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if recall == True:
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recall_score = eval_recall(gold_results[label],
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test_results[label])
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metrics_results['Recall [{0}]'.format(label)] = recall_score
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if f_measure == True:
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f_measure_score = eval_f_measure(gold_results[label],
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test_results[label])
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metrics_results['F-measure [{0}]'.format(label)] = f_measure_score
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# Print evaluation results (in alphabetical order)
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if verbose == True:
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for result in sorted(metrics_results):
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print('{0}: {1}'.format(result, metrics_results[result]))
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return metrics_results
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