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!_TAG_FILE_FORMAT 2 /extended format; --format=1 will not append ;" to lines/
!_TAG_FILE_SORTED 1 /0=unsorted, 1=sorted, 2=foldcase/
!_TAG_PROGRAM_AUTHOR Darren Hiebert /dhiebert@users.sourceforge.net/
!_TAG_PROGRAM_NAME Exuberant Ctags //
!_TAG_PROGRAM_URL http://ctags.sourceforge.net /official site/
!_TAG_PROGRAM_VERSION 5.8 //
BernoulliNB 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^from sklearn.naive_bayes import BernoulliNB$/;" i
CoherenceModel 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^from gensim.models import CoherenceModel, HdpModel$/;" i
CoherenceModel 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^from gensim.models import CoherenceModel, HdpModel$/;" i
Counter 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^from collections import Counter$/;" i
Counter 10-pos-extraction/intro-nlp-spacy.py /^from collections import Counter$/;" i
DEV 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^DEV = PRO'dev.csv'$/;" v
DEV 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^DEV = PROC_DIR + 'dev.csv'$/;" v
DEV 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^DEV = PROC_DIR + 'dev.csv'$/;" v
DIR 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^DIR = 'data'$/;" v
DIR 5-bayes-sentiment/vader.py /^DIR = 'data\/'$/;" v
HdpModel 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^from gensim.models import CoherenceModel, HdpModel$/;" i
HdpModel 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^from gensim.models import CoherenceModel, HdpModel$/;" i
INCLUDE_TEST 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^INCLUDE_TEST = False # Set to True to also include the test set, results in longer run times $/;" v
INCLUDE_TEST 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^INCLUDE_TEST = False # Set to True to also include the test set, results in longer run times $/;" v
INCLUDE_TRAIN 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^INCLUDE_TRAIN = True # Include the train set by default$/;" v
INCLUDE_TRAIN 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^INCLUDE_TRAIN = True # Include the train set by default$/;" v
MultiLabelBinarizer 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^from sklearn.preprocessing import MultiLabelBinarizer$/;" i
NaiveBayesClassifier 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^from nltk.classify import NaiveBayesClassifier$/;" i
NaiveBayesClassifier 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^from nltk.classify import NaiveBayesClassifier$/;" i
OrderedDict 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^from collections import OrderedDict$/;" i
OrderedDict 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^from collections import OrderedDict$/;" i
PROC_DIR 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^PROC_DIR = 'data\/'$/;" v
PROC_DIR 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^PROC_DIR = '.\/data\/processed\/'$/;" v
PROC_DIR 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^PROC_DIR = 'data\/'$/;" v
RegexpTokenizer 4-ngrams/.ipynb_checkpoints/ngrams-checkpoint.py /^from nltk.tokenize import RegexpTokenizer$/;" i
RegexpTokenizer 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^from nltk.tokenize import RegexpTokenizer$/;" i
STOP_WORDS 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^from spacy.lang.en.stop_words import STOP_WORDS$/;" i
STOP_WORDS 10-pos-extraction/intro-nlp-spacy.py /^from spacy.lang.en.stop_words import STOP_WORDS$/;" i
SentimentAnalyzer 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^from nltk.sentiment import SentimentAnalyzer$/;" i
SentimentAnalyzer 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^from nltk.sentiment import SentimentAnalyzer$/;" i
SentimentIntensityAnalyzer 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^from nltk.sentiment.vader import SentimentIntensityAnalyzer$/;" i
SentimentIntensityAnalyzer 5-bayes-sentiment/vader.py /^from nltk.sentiment.vader import SentimentIntensityAnalyzer$/;" i
TEMP_FOLDER 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^TEMP_FOLDER = temp_file_path$/;" v
TEMP_FOLDER 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^TEMP_FOLDER = temp_file_path$/;" v
TRAIN 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^TRAIN = PROC_DIR + 'train.csv'$/;" v
TRAIN 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^TRAIN = PROC_DIR + 'train.csv'$/;" v
TRAIN 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^TRAIN = DIR + 'twitter-2016train-A.txt'$/;" v
TRAIN 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^TRAIN = PROC_DIR + 'train.csv'$/;" v
TRAIN 5-bayes-sentiment/vader.py /^TRAIN = DIR + 'twitter-2016train-A.txt'$/;" v
alpha 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ alpha = lda_alpha)$/;" v
alpha 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ alpha = lda_alpha)$/;" v
analyzer 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^analyzer = SentimentIntensityAnalyzer()$/;" v
analyzer 5-bayes-sentiment/vader.py /^analyzer = SentimentIntensityAnalyzer()$/;" v
auto_readability_index 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def auto_readability_index(doc):$/;" f
auto_readability_index 10-pos-extraction/intro-nlp-spacy.py /^def auto_readability_index(doc):$/;" f
avg_doc_length 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^avg_doc_length = tweets['doc_length'].mean() $/;" v
avg_doc_length 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^avg_doc_length = tweets['doc_length'].mean() $/;" v
avg_sentence_length 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def avg_sentence_length(doc):$/;" f
avg_sentence_length 10-pos-extraction/intro-nlp-spacy.py /^def avg_sentence_length(doc):$/;" f
big_regex 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^big_regex = ('|').join(patterns)$/;" v
big_regex 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^big_regex="|".join(regexes)$/;" v
big_regex 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^big_regex = ('|').join(patterns)$/;" v
bnbc 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^bnbc = BernoulliNB(binarize=None)$/;" v
bow 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ bow = dictionary.doc2bow(parsed_tweets['Tweet'][i])$/;" v
bow 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ bow = dictionary.doc2bow(parsed_tweets['Tweet'][i])$/;" v
bush_speech 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^bush_speech = get_speech(bush_url)$/;" v
bush_speech 10-pos-extraction/intro-nlp-spacy.py /^bush_speech = get_speech(bush_url)$/;" v
bush_url 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^bush_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/bush2008.txt"$/;" v
bush_url 10-pos-extraction/intro-nlp-spacy.py /^bush_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/bush2008.txt"$/;" v
by_topic 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^by_topic = sns.countplot(x='LDAtopic', data=parsed_tweets)$/;" v
by_topic 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^by_topic = sns.countplot(x='topic', data=tweets)$/;" v
by_topic 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^by_topic = sns.countplot(x='LDAtopic', data=parsed_tweets)$/;" v
by_topic 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^by_topic = sns.countplot(x='topic', data=tweets)$/;" v
classifier 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^classifier = sentiment_analyzer.train(trainer, training_features)$/;" v
classifier 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^classifier = sentiment_analyzer.train(trainer, training_features)$/;" v
clinton_speech 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^clinton_speech = get_speech(clinton_url)$/;" v
clinton_speech 10-pos-extraction/intro-nlp-spacy.py /^clinton_speech = get_speech(clinton_url)$/;" v
clinton_speech_words 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^clinton_speech_words = return_words(doc)$/;" v
clinton_speech_words 10-pos-extraction/intro-nlp-spacy.py /^clinton_speech_words = return_words(doc)$/;" v
clinton_url 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^clinton_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/clinton2000.txt"$/;" v
clinton_url 10-pos-extraction/intro-nlp-spacy.py /^clinton_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/clinton2000.txt"$/;" v
cm 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ cm = CoherenceModel(model=topic_lda, corpus=corpus, dictionary=dictionary, coherence='u_mass')$/;" v
cm 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ cm = CoherenceModel(model=topic_lda, corpus=corpus, dictionary=dictionary, coherence='u_mass')$/;" v
coherence_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^coherence_lda = []$/;" v
coherence_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^coherence_lda = []$/;" v
coleman_liau_index 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def coleman_liau_index(doc, words):$/;" f
coleman_liau_index 10-pos-extraction/intro-nlp-spacy.py /^def coleman_liau_index(doc, words):$/;" f
corpora 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^from gensim import corpora, models, similarities$/;" i
corpora 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^from gensim import corpora, models, similarities$/;" i
corpus 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^corpus = textacy.Corpus('en', txt_speeches)$/;" v
corpus 10-pos-extraction/intro-nlp-spacy.py /^corpus = textacy.Corpus('en', txt_speeches)$/;" v
corpus 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^corpus = []$/;" v
corpus 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^corpus = [dictionary.doc2bow(text) for text in texts]$/;" v
corpus 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^corpus = []$/;" v
corpus 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^corpus = [dictionary.doc2bow(text) for text in texts]$/;" v
corpus_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ corpus_lda = topic_lda[corpus] # Use the bow corpus$/;" v
corpus_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^corpus_lda = lda[corpus] # Use the bow corpus$/;" v
corpus_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ corpus_lda = topic_lda[corpus] # Use the bow corpus$/;" v
corpus_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^corpus_lda = lda[corpus] # Use the bow corpus$/;" v
corpus_tfidf 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^corpus_tfidf = tfidf[corpus] # step 2 -- use the model to transform vectors$/;" v
corpus_tfidf 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^corpus_tfidf = tfidf[corpus] # step 2 -- use the model to transform vectors$/;" v
count_chars 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def count_chars(words):$/;" f
count_chars 10-pos-extraction/intro-nlp-spacy.py /^def count_chars(words):$/;" f
counter 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^counter = Counter(words)$/;" v
counter 10-pos-extraction/intro-nlp-spacy.py /^counter = Counter(words)$/;" v
custom_stopwords 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^custom_stopwords = [',', '-', '.', '’s', '-', ' ', '(', ')', '--', '---', 'n’t', ';', "'s", "'ve", " ", "’ve"]$/;" v
custom_stopwords 10-pos-extraction/intro-nlp-spacy.py /^custom_stopwords = [',', '-', '.', '’s', '-', ' ', '(', ')', '--', '---', 'n’t', ';', "'s", "'ve", " ", "’ve"]$/;" v
data 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^ data = file.readlines()$/;" v
data 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^ data = file.readlines()$/;" v
data_file_path 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^data_file_path = "data\/ANLY580\/data"$/;" v
data_file_path 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^data_file_path = "data"$/;" v
data_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^data_lda = {i: OrderedDict(lda.show_topic(i,20)) for i in range(total_topics)}$/;" v
data_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^data_lda = {i: OrderedDict(lda.show_topic(i,20)) for i in range(total_topics)}$/;" v
datafile 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^datafile = os.path.join(data_file_path, "2017_English_final\/GOLD\/Subtasks_BD\/twitter-2016test-BD.txt")$/;" v
datafile 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^datafile = os.path.join(data_file_path, "2017_English_final\/GOLD\/Subtasks_BD\/twitter-2016train-BD.txt")$/;" v
datafile 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^datafile = os.path.join(data_file_path, "2017_English_final\/GOLD\/Subtasks_BD\/twitter-2016test-BD.txt")$/;" v
datafile 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^datafile = os.path.join(data_file_path, "2017_English_final\/GOLD\/Subtasks_BD\/twitter-2016train-BD.txt")$/;" v
df2 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^df2 = parsed_tweets.groupby(['LDAtopic', 'sentiment'])['LDAtopic'].count().unstack('sentiment')$/;" v
df2 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^df2 = parsed_tweets.groupby(['LDAtopic', 'topic'])['LDAtopic'].count().unstack('topic')$/;" v
df2 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^df2 = parsed_tweets.groupby(['LDAtopic', 'sentiment'])['LDAtopic'].count().unstack('sentiment')$/;" v
df2 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^df2 = parsed_tweets.groupby(['LDAtopic', 'topic'])['LDAtopic'].count().unstack('topic')$/;" v
df_dev 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^df_dev = pd.read_csv(DEV)$/;" v
df_dev 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^df_dev = pd.read_csv(DEV)$/;" v
df_dev 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^df_dev = pd.read_csv(DEV)$/;" v
df_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^df_lda = df_lda.fillna(0).T$/;" v
df_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^df_lda = pd.DataFrame(data_lda)$/;" v
df_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^df_lda = df_lda.fillna(0).T$/;" v
df_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^df_lda = pd.DataFrame(data_lda)$/;" v
df_sentiment 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^df_sentiment = tweets.groupby(['topic', 'sentiment'])['topic'].count().unstack('sentiment')$/;" v
df_sentiment 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^df_sentiment = tweets.groupby(['topic', 'sentiment'])['topic'].count().unstack('sentiment')$/;" v
df_train 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^df_train = pd.DataFrame(df_train,columns=['id','label','text'])$/;" v
df_train 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^df_train = pd.read_csv(TRAIN)$/;" v
df_train 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^df_train = pd.DataFrame(df_train,columns=['id','label','text'])$/;" v
df_train 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^df_train = pd.read_csv(TRAIN)$/;" v
df_train 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^df_train = pd.DataFrame(df_train,columns=['id','label','text'])$/;" v
df_train 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^df_train = pd.read_csv(TRAIN)$/;" v
dictionary 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^dictionary = corpora.Dictionary(texts)$/;" v
dictionary 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^dictionary = corpora.Dictionary(texts)$/;" v
displacy 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^from spacy import displacy$/;" i
displacy 10-pos-extraction/intro-nlp-spacy.py /^from spacy import displacy$/;" i
doc 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^doc = nlp(clinton_speech)$/;" v
doc 10-pos-extraction/intro-nlp-spacy.py /^doc = nlp(clinton_speech)$/;" v
doc1 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^doc1 = nlp('There have been many mice and geese surrounding the pond.')$/;" v
doc1 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^doc1 = nlp('here are octopi')$/;" v
doc1 10-pos-extraction/intro-nlp-spacy.py /^doc1 = nlp('There have been many mice and geese surrounding the pond.')$/;" v
doc1 10-pos-extraction/intro-nlp-spacy.py /^doc1 = nlp('here are octopi')$/;" v
doc_topics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ doc_topics = lda.get_document_topics(bow, minimum_probability = 0.01)$/;" v
doc_topics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ doc_topics = lda.get_document_topics(bow, minimum_probability = 0.01)$/;" v
dtype 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ dtype = {'msgid':str, 'topic':str, 'sentiment':str, 'Tweet':str})$/;" v
dtype 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ dtype = {'msgid':str, 'topic':str, 'sentiment':str, 'Tweet':str})$/;" v
emoticon_regex 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^emoticon_regex = regex.compile(r'^' + emoticons_str + '$', regex.VERBOSE | regex.IGNORECASE)$/;" v
encoding 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ encoding = 'utf-8', $/;" v
encoding 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ encoding = 'utf-8', $/;" v
extensible_tokens 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^extensible_tokens=[]$/;" v
fancy_doc 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^fancy_doc = nlp("Regional ontology, clearly defined by Heidegger, equals, if not surpasses, the earlier work of Heidegger's own mentor, Husserl")$/;" v
fancy_doc 10-pos-extraction/intro-nlp-spacy.py /^fancy_doc = nlp("Regional ontology, clearly defined by Heidegger, equals, if not surpasses, the earlier work of Heidegger's own mentor, Husserl")$/;" v
fancy_words 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^fancy_words = return_words(fancy_doc)$/;" v
fancy_words 10-pos-extraction/intro-nlp-spacy.py /^fancy_words = return_words(fancy_doc)$/;" v
features 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^def features(sentence):$/;" f
features 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^features = df_train.iloc[:, 2].values$/;" v
features 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^def features(sentence):$/;" f
first_sent 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^first_sent = list(doc.sents)[0]$/;" v
first_sent 10-pos-extraction/intro-nlp-spacy.py /^first_sent = list(doc.sents)[0]$/;" v
gensim 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import gensim$/;" i
gensim 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import pyLDAvis.gensim$/;" i
gensim 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import gensim$/;" i
gensim 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import pyLDAvis.gensim$/;" i
get_lexicon 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def get_lexicon(text, n):$/;" f
get_lexicon 10-pos-extraction/intro-nlp-spacy.py /^def get_lexicon(text, n):$/;" f
get_speech 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def get_speech(url):$/;" f
get_speech 10-pos-extraction/intro-nlp-spacy.py /^def get_speech(url):$/;" f
get_text 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def get_text(url):$/;" f
get_text 10-pos-extraction/intro-nlp-spacy.py /^def get_text(url):$/;" f
hdp_topics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^hdp_topics = hdpmodel.get_topics()$/;" v
hdp_topics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^hdp_topics = hdpmodel.get_topics()$/;" v
hdpmodel 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^hdpmodel = HdpModel(corpus=corpus, id2word=dictionary)$/;" v
hdpmodel 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^hdpmodel = HdpModel(corpus=corpus, id2word=dictionary)$/;" v
hdptopics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^hdptopics = hdpmodel.show_topics(num_topics = 20, formatted=True)$/;" v
hdptopics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^hdptopics = hdpmodel.show_topics(num_topics = 20, formatted=True)$/;" v
header 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ header = None,$/;" v
header 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ header = None,$/;" v
human_sentiment 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^human_sentiment = list(set(parsed_tweets['sentiment'].tolist()))$/;" v
human_sentiment 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^human_sentiment = list(set(tweets['sentiment'].tolist()))$/;" v
human_sentiment 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^human_sentiment = list(set(parsed_tweets['sentiment'].tolist()))$/;" v
human_sentiment 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^human_sentiment = list(set(tweets['sentiment'].tolist()))$/;" v
human_topics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^human_topics = list(set(tweets['topic'].tolist()))$/;" v
human_topics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^human_topics = list(set(tweets['topic'].tolist()))$/;" v
id2word 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ id2word = dictionary,$/;" v
id2word 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ id2word = dictionary,$/;" v
index 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ index = topic_count_lda)$/;" v
index 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ index = topic_count_lda)$/;" v
index_col 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ index_col = False,$/;" v
index_col 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ index_col = False,$/;" v
input 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^input = "🥰 Hey! @sima #roadtrip from 09\/09-9\/27, aren't you in dude :-D ?!"$/;" v
input 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^input = "🥰 Hey! @sima #roadtrip from 09\/09-9\/27, aren't you in dude :-D ?!"$/;" v
iterations 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ iterations = 1000,$/;" v
iterations 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ iterations = 1000,$/;" v
labels 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^labels = df_train.iloc[:, 1].values$/;" v
lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^lda = models.LdaModel(corpus, id2word = dictionary, num_topics = total_topics, iterations = 1000, alpha=lda_alpha)$/;" v
lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^lda = models.LdaModel(corpus, id2word = dictionary, num_topics = total_topics, iterations = 1000, alpha=lda_alpha)$/;" v
lda_alpha 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^lda_alpha = 'auto' #learns asymmetic prior from the corpus$/;" v
lda_alpha 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^lda_alpha = 'symmetric'$/;" v
lda_alpha 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^lda_alpha = 'auto' #learns asymmetic prior from the corpus$/;" v
lda_alpha 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^lda_alpha = 'symmetric'$/;" v
letters_per_100 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def letters_per_100(words):$/;" f
letters_per_100 10-pos-extraction/intro-nlp-spacy.py /^def letters_per_100(words):$/;" f
lexical_richness 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def lexical_richness(doc):$/;" f
lexical_richness 10-pos-extraction/intro-nlp-spacy.py /^def lexical_richness(doc):$/;" f
lines 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^lines = topics_lda.plot.line(subplots = True)$/;" v
lines 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^lines = topics_lda.plot.line(subplots = True)$/;" v
list1 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^list1 = ['RT','rt', '&', 'im', 'b4', 'yr', 'nd', 'rd', 'oh', "can't", "he's", "i'll",$/;" v
list1 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^list1 = ['RT','rt', '&', 'im', 'b4', 'yr', 'nd', 'rd', 'oh', "can't", "he's", "i'll",$/;" v
matplotlib 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^import matplotlib.pyplot as plt$/;" i
matplotlib 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import matplotlib.pyplot as plt$/;" i
matplotlib 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import matplotlib.pyplot as plt$/;" i
max_doc_length 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^max_doc_length = tweets['doc_length'].max()$/;" v
max_doc_length 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^max_doc_length = tweets['doc_length'].max()$/;" v
max_to_show 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^max_to_show = 20$/;" v
max_to_show 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^max_to_show = 20$/;" v
median_doc_length 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^median_doc_length = tweets['doc_length'].median()$/;" v
median_doc_length 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^median_doc_length = tweets['doc_length'].median()$/;" v
min_doc_length 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^min_doc_length = tweets['doc_length'].min()$/;" v
min_doc_length 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^min_doc_length = tweets['doc_length'].min()$/;" v
models 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^from gensim import corpora, models, similarities$/;" i
models 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^from gensim import corpora, models, similarities$/;" i
most_common_words 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def most_common_words(doc, n):$/;" f
most_common_words 10-pos-extraction/intro-nlp-spacy.py /^def most_common_words(doc, n):$/;" f
my_extensible_tokenize 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^def my_extensible_tokenize(text):$/;" f
my_extensible_tokenizer 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^my_extensible_tokenizer = re.compile(big_regex, re.VERBOSE | re.I | re.UNICODE)$/;" v
my_lines 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^ my_lines = [next(dataset) for x in range(10)]$/;" v
my_lines 5-bayes-sentiment/vader.py /^ my_lines = [next(dataset) for x in range(10)]$/;" v
my_sentence 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^ my_sentence = my_sentence.strip().split('\\t')$/;" v
my_sentence 5-bayes-sentiment/vader.py /^ my_sentence = my_sentence.strip().split('\\t')$/;" v
names 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ names = ['msgid', 'topic', 'sentiment', 'Tweet'], $/;" v
names 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ names = ['msgid', 'topic', 'sentiment', 'Tweet'], $/;" v
neg_tweets 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^neg_tweets = df_neg_train['text'].tolist()$/;" v
neg_tweets 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^neg_tweets = df_neg_train['text'].tolist()$/;" v
negative_featuresets 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^negative_featuresets = [(features(tweet),'negative') for tweet in neg_tweets]$/;" v
negative_featuresets 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^negative_featuresets = [(features(tweet),'negative') for tweet in neg_tweets]$/;" v
neutral_featuresets 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^neutral_featuresets = [(features(tweet),'neutral') for tweet in neutral_tweets]$/;" v
neutral_featuresets 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^neutral_featuresets = [(features(tweet),'neutral') for tweet in neutral_tweets]$/;" v
neutral_tweets 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^neutral_tweets = df_neutral_train['text'].tolist()$/;" v
neutral_tweets 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^neutral_tweets = df_neutral_train['text'].tolist()$/;" v
new_doc 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^new_doc = nlp(sample_sents)$/;" v
new_doc 10-pos-extraction/intro-nlp-spacy.py /^new_doc = nlp(sample_sents)$/;" v
next_doc 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^next_doc = nlp(str(next_sent))$/;" v
next_doc 10-pos-extraction/intro-nlp-spacy.py /^next_doc = nlp(str(next_sent))$/;" v
next_sent 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^next_sent = list(doc.sents)[3]$/;" v
next_sent 10-pos-extraction/intro-nlp-spacy.py /^next_sent = list(doc.sents)[3]$/;" v
nlp 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^nlp = spacy.load('en')$/;" v
nlp 10-pos-extraction/intro-nlp-spacy.py /^nlp = spacy.load('en')$/;" v
nltk 4-ngrams/.ipynb_checkpoints/ngrams-checkpoint.py /^import nltk$/;" i
nltk 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^import nltk$/;" i
nltk 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^import nltk $/;" i
nltk 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^import nltk$/;" i
nltk 5-bayes-sentiment/vader.py /^import nltk$/;" i
nltk_casual_tokens 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^nltk_casual_tokens=[]$/;" v
nltk_casual_tokens 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^nltk_casual_tokens=[]$/;" v
np 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^import numpy as np $/;" i
np 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import numpy as np$/;" i
np 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import numpy as np$/;" i
num_topics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ num_topics = total_topics,$/;" v
num_topics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ num_topics = total_topics,$/;" v
obama_clean_speech 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^obama_clean_speech = obama_speech.replace("(Applause.)", "")$/;" v
obama_clean_speech 10-pos-extraction/intro-nlp-spacy.py /^obama_clean_speech = obama_speech.replace("(Applause.)", "")$/;" v
obama_speech 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^obama_speech = get_speech(obama_url)$/;" v
obama_speech 10-pos-extraction/intro-nlp-spacy.py /^obama_speech = get_speech(obama_url)$/;" v
obama_url 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^obama_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/obama2016.txt"$/;" v
obama_url 10-pos-extraction/intro-nlp-spacy.py /^obama_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/obama2016.txt"$/;" v
onehot_enc 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^onehot_enc = MultiLabelBinarizer()$/;" v
options 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^options = {"compact": True, 'bg': '#09a3d5',$/;" v
options 10-pos-extraction/intro-nlp-spacy.py /^options = {"compact": True, 'bg': '#09a3d5',$/;" v
os 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import os$/;" i
os 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import os$/;" i
outliers 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^outliers = tweets_filtered['Tweet'].tolist()$/;" v
outliers 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^outliers = tweets_filtered['Tweet'].tolist()$/;" v
parsed_tweets 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^parsed_tweets = tweets.filter(['msgid', 'topic', 'sentiment'], axis =1)$/;" v
parsed_tweets 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^parsed_tweets = tweets.filter(['msgid', 'topic', 'sentiment'], axis =1)$/;" v
patterns 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^patterns = [$/;" v
patterns 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^patterns = [$/;" v
pd 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^import pandas as pd$/;" i
pd 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^import pandas as pd $/;" i
pd 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^import pandas as pd$/;" i
pd 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^import pandas as pd$/;" i
pd 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^import pandas as pd$/;" i
pd 5-bayes-sentiment/vader.py /^import pandas as pd$/;" i
pd 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import pandas as pd$/;" i
pd 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import pandas as pd$/;" i
perplexity_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^perplexity_lda = []$/;" v
perplexity_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^perplexity_lda = []$/;" v
plot_size 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^plot_size = plt.rcParams["figure.figsize"] $/;" v
plt 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^import matplotlib.pyplot as plt$/;" i
plt 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import matplotlib.pyplot as plt$/;" i
plt 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import matplotlib.pyplot as plt$/;" i
pos_tweets 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^pos_tweets = df_pos_train['text'].tolist()$/;" v
pos_tweets 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^pos_tweets = df_pos_train['text'].tolist()$/;" v
positive_featuresets 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^positive_featuresets = [(features(tweet),'positive') for tweet in pos_tweets]$/;" v
positive_featuresets 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^positive_featuresets = [(features(tweet),'positive') for tweet in pos_tweets]$/;" v
pprint 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import pprint$/;" i
pprint 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import pprint$/;" i
probs 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^probs = []$/;" v
probs 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^probs = []$/;" v
processed_feature 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^ processed_feature = processed_feature.lower()$/;" v
processed_feature 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^ processed_feature = re.sub(r'\\W', ' ', str(features[sentence]))$/;" v
processed_feature 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^ processed_feature = re.sub(r'\\^[a-zA-Z]\\s+', ' ', processed_feature) $/;" v
processed_feature 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^ processed_feature = re.sub(r'\\s+', ' ', processed_feature, flags=re.I)$/;" v
processed_feature 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^ processed_feature = re.sub(r'^b\\s+', '', processed_feature)$/;" v
processed_feature 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^ processed_feature= re.sub(r'\\s+[a-zA-Z]\\s+', ' ', processed_feature)$/;" v
processed_features 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^processed_features = []$/;" v
punctuation 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^punctuation = list(string.punctuation)$/;" v
pyLDAvis 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import pyLDAvis.gensim$/;" i
pyLDAvis 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import pyLDAvis.gensim$/;" i
python.pythonPath .vscode/settings.json /^ "python.pythonPath": "\/Users\/lisa\/anaconda3\/bin\/python"$/;" f
re 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^import re$/;" i
re 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import re$/;" i
re 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import re$/;" i
regex 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^import regex$/;" i
regex 4-ngrams/.ipynb_checkpoints/ngrams-checkpoint.py /^import regex$/;" i
regex 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^import regex$/;" i
regex_str 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^regex_str = [$/;" v
regexes 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^regexes=(r"(?:@[\\w_]+)",$/;" v
requests 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^import requests$/;" i
requests 10-pos-extraction/intro-nlp-spacy.py /^import requests$/;" i
return_words 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def return_words(doc):$/;" f
return_words 10-pos-extraction/intro-nlp-spacy.py /^def return_words(doc):$/;" f
sample_sents 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^sample_sents = "One fish, two fish, red fish, blue fish. One is less than two."$/;" v
sample_sents 10-pos-extraction/intro-nlp-spacy.py /^sample_sents = "One fish, two fish, red fish, blue fish. One is less than two."$/;" v
score 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^score = bnbc.score(onehot_enc.transform(X_test), y_test)$/;" v
se 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^se = pd.Series(texts)$/;" v
se 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^se = pd.Series(texts)$/;" v
sentences 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^sentences = ["VADER is smart, handsome, and funny.", # positive sentence example$/;" v
sentences 5-bayes-sentiment/vader.py /^sentences = ["VADER is smart, handsome, and funny.", # positive sentence example$/;" v
sentences_per_100 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^def sentences_per_100(doc, words):$/;" f
sentences_per_100 10-pos-extraction/intro-nlp-spacy.py /^def sentences_per_100(doc, words):$/;" f
sentiment_analyzer 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^sentiment_analyzer = SentimentAnalyzer()$/;" v
sentiment_analyzer 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^sentiment_analyzer = SentimentAnalyzer()$/;" v
sep 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ sep = '\\t', $/;" v
sep 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ sep = '\\t', $/;" v
similarities 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^from gensim import corpora, models, similarities$/;" i
similarities 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^from gensim import corpora, models, similarities$/;" i
single_doc 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^single_doc = nlp(str(first_sent))$/;" v
single_doc 10-pos-extraction/intro-nlp-spacy.py /^single_doc = nlp(str(first_sent))$/;" v
sns 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import seaborn as sns$/;" i
sns 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import seaborn as sns$/;" i
spacy 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^import spacy$/;" i
spacy 10-pos-extraction/intro-nlp-spacy.py /^import spacy$/;" i
speeches 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^speeches = {$/;" v
speeches 10-pos-extraction/intro-nlp-spacy.py /^speeches = {$/;" v
stats 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^ stats = textacy.text_stats.TextStats(doc)$/;" v
stats 10-pos-extraction/intro-nlp-spacy.py /^ stats = textacy.text_stats.TextStats(doc)$/;" v
stoplist 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^stoplist = stopwords.words('english') + list(string.punctuation) + list1$/;" v
stoplist 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^stoplist = stopwords.words('english') + list(string.punctuation) + list1$/;" v
stopwords 4-ngrams/.ipynb_checkpoints/ngrams-checkpoint.py /^from nltk.corpus import stopwords$/;" i
stopwords 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^from nltk.corpus import stopwords$/;" i
stopwords 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^from nltk.corpus import stopwords$/;" i
stopwords 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^from nltk.corpus import stopwords$/;" i
string 4-ngrams/.ipynb_checkpoints/ngrams-checkpoint.py /^import string$/;" i
string 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^import string$/;" i
string 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import string$/;" i
string 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import string$/;" i
temp_file_path 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^temp_file_path = "data\/ANLY580\/backup"$/;" v
temp_file_path 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^temp_file_path = "backup"$/;" v
text_feats 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^ text_feats = features(text)$/;" v
text_feats 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^ text_feats = features(text)$/;" v
text_length 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^text_length = []$/;" v
text_length 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^text_length = []$/;" v
textacy 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^import textacy$/;" i
textacy 10-pos-extraction/intro-nlp-spacy.py /^import textacy$/;" i
texts 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^texts = [[word for word in str(document).lower().split() if word not in stoplist] for document in corpus]$/;" v
texts 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^texts = [[word for word in str(document).lower().split() if word not in stoplist] for document in corpus]$/;" v
tfidf 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^tfidf = models.TfidfModel(corpus) # step 1 -- initialize a model$/;" v
tfidf 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^tfidf = models.TfidfModel(corpus) # step 1 -- initialize a model$/;" v
tokenize 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^def tokenize(s):$/;" f
tokens 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^tokens = regex.findall(big_regex,input)$/;" v
tokens 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^tokens = [term for term in tokens if not term.startswith('#')]$/;" v
tokens 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^tokens = [term for term in tokens if not term.startswith('@')]$/;" v
tokens 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^tokens = [term for term in tokens if term not in punctuation]$/;" v
tokens 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^tokens = [term.lower() for term in tokens if term.lower() not in stopwords.words('english')]$/;" v
tokens 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^tokens = nltk.casual_tokenize(input)$/;" v
tokens 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^tokens = regex.findall(big_regex,input)$/;" v
tokens 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^ tokens = processed_feature.split()$/;" v
tokens_regex 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^tokens_regex = regex.compile(r'(' + '|'.join(regex_str) + ')', regex.VERBOSE | regex.IGNORECASE)$/;" v
topic_count_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^topic_count_lda = []$/;" v
topic_count_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^topic_count_lda = []$/;" v
topic_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ topic_lda = models.LdaModel(corpus,$/;" v
topic_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ topic_lda = models.LdaModel(corpus,$/;" v
topic_mixture 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^topic_mixture = df2[human_sentiment].plot(kind='bar', stacked=True, legend = True)$/;" v
topic_mixture 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^topic_mixture = df2[human_topics].plot(kind='bar', stacked=True, legend = False)$/;" v
topic_mixture 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^topic_mixture = df_sentiment[human_sentiment].plot(kind='bar', stacked=True, legend = True)$/;" v
topic_mixture 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^topic_mixture = df2[human_sentiment].plot(kind='bar', stacked=True, legend = True)$/;" v
topic_mixture 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^topic_mixture = df2[human_topics].plot(kind='bar', stacked=True, legend = False)$/;" v
topic_mixture 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^topic_mixture = df_sentiment[human_sentiment].plot(kind='bar', stacked=True, legend = True)$/;" v
topics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^topics = []$/;" v
topics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^topics = []$/;" v
topics_lda 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^topics_lda = pd.DataFrame({'perplexity': perplexity_lda,$/;" v
topics_lda 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^topics_lda = pd.DataFrame({'perplexity': perplexity_lda,$/;" v
topics_sorted 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ topics_sorted = sorted(doc_topics, key = lambda x: x[1], reverse = True)$/;" v
topics_sorted 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ topics_sorted = sorted(doc_topics, key = lambda x: x[1], reverse = True)$/;" v
total_topics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^total_topics = 20$/;" v
total_topics 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^total_topics = len(human_topics)$/;" v
total_topics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^total_topics = 20$/;" v
total_topics 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^total_topics = len(human_topics)$/;" v
train_test_split 5-bayes-sentiment/.ipynb_checkpoints/sentiment_scikitlearn-checkpoint.py /^from sklearn.model_selection import train_test_split$/;" i
trainer 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^trainer = NaiveBayesClassifier.train$/;" v
trainer 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^trainer = NaiveBayesClassifier.train$/;" v
training_features 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^training_features = positive_featuresets + negative_featuresets + neutral_featuresets$/;" v
training_features 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^training_features = positive_featuresets + negative_featuresets + neutral_featuresets$/;" v
trump_speech 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^trump_speech = get_speech(trump_url)$/;" v
trump_speech 10-pos-extraction/intro-nlp-spacy.py /^trump_speech = get_speech(trump_url)$/;" v
trump_url 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^trump_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/trump.txt"$/;" v
trump_url 10-pos-extraction/intro-nlp-spacy.py /^trump_url = "https:\/\/raw.githubusercontent.com\/sul-cidr\/python_workshops\/master\/data\/trump.txt"$/;" v
truth_list 5-bayes-sentiment/.ipynb_checkpoints/sentiment_nltk_naivebayes-checkpoint.py /^truth_list = list(df_dev[['text', 'label']].itertuples(index=False, name=None))$/;" v
truth_list 5-bayes-sentiment/sentiment_nltk_naivebayes.py /^truth_list = list(df_dev[['text', 'label']].itertuples(index=False, name=None))$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = re.sub(r'--+', ' ', tweet)$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = re.sub(r'[0-9]+', '', tweet)$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = re.sub(r'[0-9]+GB', '', tweet)$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = re.sub(r'\\$[0-9]+', '', tweet)$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = re.sub(r'http\\S+', '', tweet)$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = tweet.replace("&", " ").replace(">", "").replace("<", "")$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = tweet.replace("(", "").replace(")", "").replace(".", "").replace("?", "").replace("!", "").replace(",", "")$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = tweet.replace("\/", " ").replace("=", "").replace('\\"', "").replace('*', '').replace(';', "")$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = tweet.replace(':', '').replace('"', '')$/;" v
tweet 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweet = tweets['Tweet'][i]$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = re.sub(r'--+', ' ', tweet)$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = re.sub(r'[0-9]+', '', tweet)$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = re.sub(r'[0-9]+GB', '', tweet)$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = re.sub(r'\\$[0-9]+', '', tweet)$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = re.sub(r'http\\S+', '', tweet)$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = tweet.replace("&", " ").replace(">", "").replace("<", "")$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = tweet.replace("(", "").replace(")", "").replace(".", "").replace("?", "").replace("!", "").replace(",", "")$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = tweet.replace("\/", " ").replace("=", "").replace('\\"', "").replace('*', '').replace(';', "")$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = tweet.replace(':', '').replace('"', '')$/;" v
tweet 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweet = tweets['Tweet'][i]$/;" v
tweets 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^ tweets=[]$/;" v
tweets 4-ngrams/.ipynb_checkpoints/pre-processing-checkpoint.py /^ tweets=[]$/;" v
tweets 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweets = pd.concat([tweets1, tweets2], ignore_index=True)$/;" v
tweets 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweets = tweets1$/;" v
tweets 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^ tweets = tweets2$/;" v
tweets 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweets = pd.concat([tweets1, tweets2], ignore_index=True)$/;" v
tweets 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweets = tweets1$/;" v
tweets 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^ tweets = tweets2$/;" v
tweets1 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^tweets1 = pd.read_csv(datafile, $/;" v
tweets1 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^tweets1 = pd.read_csv(datafile, $/;" v
tweets2 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^tweets2 = pd.read_csv(datafile, $/;" v
tweets2 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^tweets2 = pd.read_csv(datafile, $/;" v
tweets_filtered 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^tweets_filtered = tweets[tweets['doc_length'] > 40]$/;" v
tweets_filtered 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^tweets_filtered = tweets[tweets['doc_length'] > 40]$/;" v
txt_speeches 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^txt_speeches = [clinton_speech, bush_speech, obama_clean_speech, trump_speech]$/;" v
txt_speeches 10-pos-extraction/intro-nlp-spacy.py /^txt_speeches = [clinton_speech, bush_speech, obama_clean_speech, trump_speech]$/;" v
vs 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^ vs = analyzer.polarity_scores(sentence)$/;" v
vs 5-bayes-sentiment/.ipynb_checkpoints/vader-checkpoint.py /^ vs = analyzer.polarity_scores(text)$/;" v
vs 5-bayes-sentiment/vader.py /^ vs = analyzer.polarity_scores(sentence)$/;" v
vs 5-bayes-sentiment/vader.py /^ vs = analyzer.polarity_scores(text)$/;" v
warnings 8-topic-models/.ipynb_checkpoints/ANLY580_Fall2019_TopicModeling-checkpoint.py /^import warnings$/;" i
warnings 8-topic-models/ANLY580_Fall2019_TopicModeling.py /^import warnings$/;" i
whitespace_tokens 4-ngrams/.ipynb_checkpoints/4-ngrams-checkpoint.py /^whitespace_tokens=[]$/;" v
words 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^ words = return_words(speech)$/;" v
words 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^words = [token.text for token in new_doc if token.pos_ is not 'PUNCT']$/;" v
words 10-pos-extraction/.ipynb_checkpoints/intro-nlp-spacy-checkpoint.py /^words = return_words(nlp("Well, I am not 30 years old."))$/;" v
words 10-pos-extraction/intro-nlp-spacy.py /^ words = return_words(speech)$/;" v
words 10-pos-extraction/intro-nlp-spacy.py /^words = [token.text for token in new_doc if token.pos_ is not 'PUNCT']$/;" v
words 10-pos-extraction/intro-nlp-spacy.py /^words = return_words(nlp("Well, I am not 30 years old."))$/;" v