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Mix up articles into new ones (Python)

Discussion in 'Black Hat SEO Tools' started by Jespersen, Jun 10, 2014.

  1. Jespersen

    Jespersen Newbie

    Nov 2, 2013
    Likes Received:
    A quick and dirty Python script which takes a list of paragraphs (= articles) from a .txt file as input and replaces every sentence in each with similar sentences taken from other paragraphs in the collections. It doesn't spin anything (that'd be a good next step though), but the results aren't 'traceable' to a single source. Use lots of articles (>100) for more human-readable content.

    Statistical measure for sentence similarity adopted from Metzler et al. (2005).

    #!/usr/bin/env python
    import re, sys, os, math, itertools, time
    from collections import defaultdict
    def split_sentences(content):
        abbrevs = ['dr', 'mr', 'mrs', 'ms', 'prof', 'inc', 'vs', 'ex', 'e.g', 'i.e', 'ps', 'p.s', 'no'] + list('abcdefghijklmnopqrstuvwxyz')
        normalized_content = re.sub(r'(?<=[ \(])(' + r'|'.join(abbrevs) + r')\.', lambda c: c.group()[:-1] + '_', content, flags=re.I)
        return [content[sent.start():sent.end()] for sent in re.finditer(r'[^ ].+?[\.\?\!]+', normalized_content)]
    def split_words(S):
        return re.sub(r'(?<![\.\!\?])[\.\,\?\!\:\;\'\"](?![\.\!\?])', lambda c: ' ' + c.group() + ' ', S).split()
    def lemma(word):
        """ how word forms get standarized when comparing sentences - currently just lowercase, first 5 characters """
        return word.lower()[:5]
    def calculate_idf(documents):
        lemmatized_docs = [[lemma(w) for w in split_words(doc)] for doc in documents]
        df = defaultdict(int)
        for doc in lemmatized_docs:
            for type in set(doc):
                df[type] += 1
        return {w: math.log(len(documents)) / df[w] for w in df.keys()}
    def edge_similarity(s1, s2):
        e1 = s1[:2] + s1[-1:]
        e2 = s2[:2] + s2[-1:]
        aligned = zip(e1, e2)
        return float(len([e for e in aligned if e[0] == e[1]])) / len(aligned)
    def content_similarity(s1, s2, type_IDF):
        s1_tokens = [lemma(w) for w in split_words(s1)]
        s2_tokens = [lemma(w) for w in split_words(s2)]
        def w_penalty(type, tokens1, tokens2): return 1 + abs(tokens1.count(type) - tokens2.count(type))
        def s_penalty(tokens1, tokens2): return 1 + max(len(tokens1), len(tokens2)) / min(len(tokens1), len(tokens2))
        measure = sum(type_IDF[type] / w_penalty(type, s1_tokens, s2_tokens) for type in set(s1_tokens) & set(s2_tokens))
        if s1_tokens or s2_tokens:
            return measure / s_penalty(s1_tokens, s2_tokens)
            return 1
    def sentence_similarity(s1, s2, type_IDF):
        return content_similarity(s1, s2, type_IDF) + edge_similarity(s1, s2)
    class ArticleDatabase():
        def __init__(self, file_path):
                with open(file_path, 'r') as file:
                    self.articles = file.readlines()
                print "\n%i articles loaded." % len(self.articles)
            except IOError:
                sys.exit("\nNo articles could be loaded - couldn't find path: %s" % file_path)
            self.idf = calculate_idf(self.articles)
            self.initial_sents = {}
            self.mainbody_sents = {}
            self.final_sents = {}
            for article_id, article in enumerate(self.articles):
                sents = split_sentences(article)
                self.initial_sents[article_id] = sents[:1]
                self.mainbody_sents[article_id] = sents[1:-1]
                self.final_sents[article_id] = sents[-1:]
        def recreate_article(self, article_id):
            print "Recreating article no. %i" % (article_id + 1)
            target_sents = split_sentences(self.articles[article_id])
            new_art = []
            source_mainbody_sents = set(itertools.chain(*[v for k, v in self.mainbody_sents.items() if k != article_id]))
            source_initial_sents = set(itertools.chain(*[v for k, v in self.initial_sents.items() if k != article_id]))
            source_final_sents = set(itertools.chain(*[v for k, v in self.final_sents.items() if k != article_id]))
            for i, sent in enumerate(target_sents):
                if i == 0:
                    source_set = source_initial_sents
                elif i == len(target_sents) - 1:
                    source_set = source_final_sents
                    source_set = source_mainbody_sents
                if source_set:
                    most_similar = sorted(source_set, key=lambda x: sentence_similarity(sent, x, self.idf), reverse=True)
                    replacement = most_similar[0]
                    replacement = sent
            return new_art
        def output(self, out_path, number_of_articles=10):
            results = [" ".join(self.recreate_article(article_id)) + "\n" for article_id in range(min(len(self.articles), number_of_articles))]
            with open(out_path, 'w') as out_file:
                for result in results:
    if __name__ == "__main__":
        if len(sys.argv) == 4:
            db = ArticleDatabase(sys.argv[1])
            db.output(sys.argv[2], int(sys.argv[3]))
            print "\nWrong number of arguments!\nCorrect usage:\n"
            print "python recompose.py python recompose.py <file path> <output file path> <number of articles to rewrite>"
    Result example using a bunch of fantasy book reviews:

    How to use:

    1. Save a bunch of articles (with no linebreaks inside) as separate paragraphs to a .txt file.
    2. Install Python 2.7 if you don't have it.
    3. Save the script as recompose.py (Windows users might need to use their Python or Python/Scripts folder)
    4. From the command line, run: python recompose.py <file path> <output file path> <number of articles to rewrite>, e.g.

    Hopefully you'll find this useful for something.
  2. lord1027

    lord1027 Elite Member

    Sep 20, 2013
    Likes Received:
    This looks interesting, I'll give it a try. Anyone else tried this yet?
  3. MrBlue

    MrBlue Senior Member

    Dec 18, 2009
    Likes Received:
    Web/Bot Developer
    Nice share. I built something very similar using Node.js. You should take a look at the following NLP ( Natural Language Processing) library for Python.
  4. Jespersen

    Jespersen Newbie

    Nov 2, 2013
    Likes Received:
    Yep, I normally use NLTK, it just wasn't necessary in the end because I wanted to see how simple I can keep a thing like this. Using Wordnet-based similarity measures improves accuracy, but it lowers performance approximately 20 times, so...