From: Nate E TeBlunthuis Date: Tue, 4 Aug 2020 20:24:37 +0000 (-0700) Subject: Improve tokenization following data. Generate author counts. X-Git-Url: https://code.communitydata.science/cdsc_reddit.git/commitdiff_plain/78ab514d6bdf546a1beed3d1091c6861364eb20c?ds=inline;hp=b3ffaaba1d065614f3f19ee0cbc876185dc220e1 Improve tokenization following data. Generate author counts. --- diff --git a/tf_reddit_comments.py b/tf_reddit_comments.py index ec2dd2c..3596062 100644 --- a/tf_reddit_comments.py +++ b/tf_reddit_comments.py @@ -7,12 +7,33 @@ from collections import Counter import pandas as pd import os import datetime -from nltk import wordpunct_tokenize, MWETokenizer +import re +from nltk import wordpunct_tokenize, MWETokenizer, sent_tokenize +from nltk.corpus import stopwords +from nltk.util import ngrams +import string +from random import random + +# remove urls +# taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url +urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)") # compute term frequencies for comments in each subreddit by week -def weekly_tf(partition): +def weekly_tf(partition, mwe_pass = 'first'): dataset = ds.dataset(f'/gscratch/comdata/output/reddit_comments_by_subreddit.parquet/{partition}', format='parquet') - batches = dataset.to_batches(columns=['CreatedAt','subreddit','body']) + + if not os.path.exists("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/"): + os.mkdir("/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/") + + if not os.path.exists("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/"): + os.mkdir("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/") + + ngram_output = partition.replace("parquet","txt") + + if os.path.exists(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}"): + os.remove(f"/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}") + + batches = dataset.to_batches(columns=['CreatedAt','subreddit','body','author']) schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False), pa.field('term', pa.string(), nullable=False), @@ -20,6 +41,12 @@ def weekly_tf(partition): pa.field('tf', pa.int64(), nullable=False)] ) + author_schema = pa.schema([pa.field('subreddit', pa.string(), nullable=False), + pa.field('author', pa.string(), nullable=False), + pa.field('week', pa.date32(), nullable=False), + pa.field('tf', pa.int64(), nullable=False)] + ) + dfs = (b.to_pandas() for b in batches) def add_week(df): @@ -37,34 +64,106 @@ def weekly_tf(partition): subreddit_weeks = groupby(rows, lambda r: (r.subreddit, r.week)) - tokenizer = MWETokenizer() + mwe_tokenize = MWETokenizer().tokenize + + def remove_punct(sentence): + new_sentence = [] + for token in sentence: + new_token = '' + for c in token: + if c not in string.punctuation: + new_token += c + if len(new_token) > 0: + new_sentence.append(new_token) + return new_sentence + + + stopWords = set(stopwords.words('english')) + + # we follow the approach described in datta, phelan, adar 2017 + def my_tokenizer(text): + # remove stopwords, punctuation, urls, lower case + # lowercase + text = text.lower() + + # remove urls + text = urlregex.sub("", text) + + # sentence tokenize + sentences = sent_tokenize(text) + + # wordpunct_tokenize + sentences = map(wordpunct_tokenize, sentences) + + # remove punctuation + + sentences = map(remove_punct, sentences) + + # remove sentences with less than 2 words + sentences = filter(lambda sentence: len(sentence) > 2, sentences) + + # datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase. + # they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms + # here we take a 10 percent sample of sentences + if mwe_pass == 'first': + sentences = list(sentences) + for sentence in sentences: + if random() <= 0.1: + grams = list(chain(*map(lambda i : ngrams(sentence,i),range(4)))) + with open(f'/gscratch/comdata/users/nathante/reddit_comment_ngrams_10p_sample/{ngram_output}','a') as gram_file: + for ng in grams: + gram_file.write(' '.join(ng) + '\n') + for token in sentence: + if token not in stopWords: + yield token + + else: + # remove stopWords + sentences = map(lambda s: filter(lambda token: token not in stopWords, s), sentences) + return chain(* sentences) def tf_comments(subreddit_weeks): for key, posts in subreddit_weeks: subreddit, week = key tfs = Counter([]) - + authors = Counter([]) for post in posts: - tfs.update(tokenizer.tokenize(wordpunct_tokenize(post.body.lower()))) + tokens = my_tokenizer(post.body) + tfs.update(tokens) + authors.update([post.author]) for term, tf in tfs.items(): - yield [subreddit, term, week, tf] - + yield [True, subreddit, term, week, tf] + + for author, tf in authors.items(): + yield [False, subreddit, author, week, tf] + outrows = tf_comments(subreddit_weeks) outchunksize = 10000 - with pq.ParquetWriter("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/{partition}",schema=schema,compression='snappy',flavor='spark') as writer: + with pq.ParquetWriter("/gscratch/comdata/users/nathante/reddit_tfidf_test.parquet_temp/{partition}",schema=schema,compression='snappy',flavor='spark') as writer, pq.ParquetWriter("/gscratch/comdata/users/nathante/reddit_tfidf_test_authors.parquet_temp/{partition}",schema=author_schema,compression='snappy',flavor='spark') as author_writer: while True: chunk = islice(outrows,outchunksize) - pddf = pd.DataFrame(chunk, columns=schema.names) + pddf = pd.DataFrame(chunk, columns=["is_token"] + schema.names) + print(pddf) + author_pddf = pddf.loc[pddf.is_token == False] + author_pddf = author_pddf.rename({'term':'author'}, axis='columns') + author_pddf = author_pddf.loc[:,author_schema.names] + + pddf = pddf.loc[pddf.is_token == True, schema.names] + print(pddf) + print(author_pddf) table = pa.Table.from_pandas(pddf,schema=schema) + author_table = pa.Table.from_pandas(author_pddf,schema=author_schema) if table.shape[0] == 0: break writer.write_table(table) - + author_writer.write_table(author_table) + writer.close() + author_writer.close() def gen_task_list():