Source code for nltk.corpus.reader.reviews

# Natural Language Toolkit: Product Reviews Corpus Reader
#
# Copyright (C) 2001-2017 NLTK Project
# Author: Pierpaolo Pantone <24alsecondo@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

"""
CorpusReader for reviews corpora (syntax based on Customer Review Corpus).

- Customer Review Corpus information -
Annotated by: Minqing Hu and Bing Liu, 2004.
    Department of Computer Sicence
    University of Illinois at Chicago

Contact: Bing Liu, liub@cs.uic.edu
        http://www.cs.uic.edu/~liub

Distributed with permission.

The "product_reviews_1" and "product_reviews_2" datasets respectively contain
annotated customer reviews of 5 and 9 products from amazon.com.

Related papers:

- Minqing Hu and Bing Liu. "Mining and summarizing customer reviews".
    Proceedings of the ACM SIGKDD International Conference on Knowledge
    Discovery & Data Mining (KDD-04), 2004.

- Minqing Hu and Bing Liu. "Mining Opinion Features in Customer Reviews".
    Proceedings of Nineteeth National Conference on Artificial Intelligence
    (AAAI-2004), 2004.

- Xiaowen Ding, Bing Liu and Philip S. Yu. "A Holistic Lexicon-Based Appraoch to
    Opinion Mining." Proceedings of First ACM International Conference on Web
    Search and Data Mining (WSDM-2008), Feb 11-12, 2008, Stanford University,
    Stanford, California, USA.

Symbols used in the annotated reviews:

    [t] : the title of the review: Each [t] tag starts a review.
    xxxx[+|-n]: xxxx is a product feature.
    [+n]: Positive opinion, n is the opinion strength: 3 strongest, and 1 weakest.
          Note that the strength is quite subjective.
          You may want ignore it, but only considering + and -
    [-n]: Negative opinion
    ##  : start of each sentence. Each line is a sentence.
    [u] : feature not appeared in the sentence.
    [p] : feature not appeared in the sentence. Pronoun resolution is needed.
    [s] : suggestion or recommendation.
    [cc]: comparison with a competing product from a different brand.
    [cs]: comparison with a competing product from the same brand.

Note: Some of the files (e.g. "ipod.txt", "Canon PowerShot SD500.txt") do not
    provide separation between different reviews. This is due to the fact that
    the dataset was specifically designed for aspect/feature-based sentiment
    analysis, for which sentence-level annotation is sufficient. For document-
    level classification and analysis, this peculiarity should be taken into
    consideration.
"""

from __future__ import division

import re

from nltk.corpus.reader.api import *
from nltk.tokenize import *

TITLE = re.compile(r'^\[t\](.*)$') # [t] Title
FEATURES = re.compile(r'((?:(?:\w+\s)+)?\w+)\[((?:\+|\-)\d)\]') # find 'feature' in feature[+3]
NOTES = re.compile(r'\[(?!t)(p|u|s|cc|cs)\]') # find 'p' in camera[+2][p]
SENT = re.compile(r'##(.*)$') # find tokenized sentence


@compat.python_2_unicode_compatible
[docs]class Review(object): """ A Review is the main block of a ReviewsCorpusReader. """ def __init__(self, title=None, review_lines=None): """ :param title: the title of the review. :param review_lines: the list of the ReviewLines that belong to the Review. """ self.title = title if review_lines is None: self.review_lines = [] else: self.review_lines = review_lines
[docs] def add_line(self, review_line): """ Add a line (ReviewLine) to the review. :param review_line: a ReviewLine instance that belongs to the Review. """ assert isinstance(review_line, ReviewLine) self.review_lines.append(review_line)
[docs] def features(self): """ Return a list of features in the review. Each feature is a tuple made of the specific item feature and the opinion strength about that feature. :return: all features of the review as a list of tuples (feat, score). :rtype: list(tuple) """ features = [] for review_line in self.review_lines: features.extend(review_line.features) return features
[docs] def sents(self): """ Return all tokenized sentences in the review. :return: all sentences of the review as lists of tokens. :rtype: list(list(str)) """ return [review_line.sent for review_line in self.review_lines]
def __repr__(self): return 'Review(title=\"{}\", review_lines={})'.format(self.title, self.review_lines)
@compat.python_2_unicode_compatible
[docs]class ReviewLine(object): """ A ReviewLine represents a sentence of the review, together with (optional) annotations of its features and notes about the reviewed item. """ def __init__(self, sent, features=None, notes=None): self.sent = sent if features is None: self.features = [] else: self.features = features if notes is None: self.notes = [] else: self.notes = notes def __repr__(self): return ('ReviewLine(features={}, notes={}, sent={})'.format( self.features, self.notes, self.sent))
[docs]class ReviewsCorpusReader(CorpusReader): """ Reader for the Customer Review Data dataset by Hu, Liu (2004). Note: we are not applying any sentence tokenization at the moment, just word tokenization. >>> from nltk.corpus import product_reviews_1 >>> camera_reviews = product_reviews_1.reviews('Canon_G3.txt') >>> review = camera_reviews[0] >>> review.sents()[0] ['i', 'recently', 'purchased', 'the', 'canon', 'powershot', 'g3', 'and', 'am', 'extremely', 'satisfied', 'with', 'the', 'purchase', '.'] >>> review.features() [('canon powershot g3', '+3'), ('use', '+2'), ('picture', '+2'), ('picture quality', '+1'), ('picture quality', '+1'), ('camera', '+2'), ('use', '+2'), ('feature', '+1'), ('picture quality', '+3'), ('use', '+1'), ('option', '+1')] We can also reach the same information directly from the stream: >>> product_reviews_1.features('Canon_G3.txt') [('canon powershot g3', '+3'), ('use', '+2'), ...] We can compute stats for specific product features: >>> from __future__ import division >>> n_reviews = len([(feat,score) for (feat,score) in product_reviews_1.features('Canon_G3.txt') if feat=='picture']) >>> tot = sum([int(score) for (feat,score) in product_reviews_1.features('Canon_G3.txt') if feat=='picture']) >>> # We use float for backward compatibility with division in Python2.7 >>> mean = tot / n_reviews >>> print(n_reviews, tot, mean) 15 24 1.6 """ CorpusView = StreamBackedCorpusView def __init__(self, root, fileids, word_tokenizer=WordPunctTokenizer(), encoding='utf8'): """ :param root: The root directory for the corpus. :param fileids: a list or regexp specifying the fileids in the corpus. :param word_tokenizer: a tokenizer for breaking sentences or paragraphs into words. Default: `WordPunctTokenizer` :param encoding: the encoding that should be used to read the corpus. """ CorpusReader.__init__(self, root, fileids, encoding) self._word_tokenizer = word_tokenizer
[docs] def features(self, fileids=None): """ Return a list of features. Each feature is a tuple made of the specific item feature and the opinion strength about that feature. :param fileids: a list or regexp specifying the ids of the files whose features have to be returned. :return: all features for the item(s) in the given file(s). :rtype: list(tuple) """ if fileids is None: fileids = self._fileids elif isinstance(fileids, string_types): fileids = [fileids] return concat([self.CorpusView(fileid, self._read_features, encoding=enc) for (fileid, enc) in self.abspaths(fileids, True)])
[docs] def raw(self, fileids=None): """ :param fileids: a list or regexp specifying the fileids of the files that have to be returned as a raw string. :return: the given file(s) as a single string. :rtype: str """ if fileids is None: fileids = self._fileids elif isinstance(fileids, string_types): fileids = [fileids] return concat([self.open(f).read() for f in fileids])
[docs] def readme(self): """ Return the contents of the corpus README.txt file. """ return self.open("README.txt").read()
[docs] def reviews(self, fileids=None): """ Return all the reviews as a list of Review objects. If `fileids` is specified, return all the reviews from each of the specified files. :param fileids: a list or regexp specifying the ids of the files whose reviews have to be returned. :return: the given file(s) as a list of reviews. """ if fileids is None: fileids = self._fileids return concat([self.CorpusView(fileid, self._read_review_block, encoding=enc) for (fileid, enc) in self.abspaths(fileids, True)])
[docs] def sents(self, fileids=None): """ Return all sentences in the corpus or in the specified files. :param fileids: a list or regexp specifying the ids of the files whose sentences have to be returned. :return: the given file(s) as a list of sentences, each encoded as a list of word strings. :rtype: list(list(str)) """ return concat([self.CorpusView(path, self._read_sent_block, encoding=enc) for (path, enc, fileid) in self.abspaths(fileids, True, True)])
[docs] def words(self, fileids=None): """ Return all words and punctuation symbols in the corpus or in the specified files. :param fileids: a list or regexp specifying the ids of the files whose words have to be returned. :return: the given file(s) as a list of words and punctuation symbols. :rtype: list(str) """ return concat([self.CorpusView(path, self._read_word_block, encoding=enc) for (path, enc, fileid) in self.abspaths(fileids, True, True)])
def _read_features(self, stream): features = [] for i in range(20): line = stream.readline() if not line: return features features.extend(re.findall(FEATURES, line)) return features def _read_review_block(self, stream): while True: line = stream.readline() if not line: return [] # end of file. title_match = re.match(TITLE, line) if title_match: review = Review(title=title_match.group(1).strip()) # We create a new review break # Scan until we find another line matching the regexp, or EOF. while True: oldpos = stream.tell() line = stream.readline() # End of file: if not line: return [review] # Start of a new review: backup to just before it starts, and # return the review we've already collected. if re.match(TITLE, line): stream.seek(oldpos) return [review] # Anything else is part of the review line. feats = re.findall(FEATURES, line) notes = re.findall(NOTES, line) sent = re.findall(SENT, line) if sent: sent = self._word_tokenizer.tokenize(sent[0]) review_line = ReviewLine(sent=sent, features=feats, notes=notes) review.add_line(review_line) def _read_sent_block(self, stream): sents = [] for review in self._read_review_block(stream): sents.extend([sent for sent in review.sents()]) return sents def _read_word_block(self, stream): words = [] for i in range(20): # Read 20 lines at a time. line = stream.readline() sent = re.findall(SENT, line) if sent: words.extend(self._word_tokenizer.tokenize(sent[0])) return words