def get_most_frequent_words(text):
word_list = tokenize_nltk(text)
word_list = [
word for word in word_list if word not in stopwords
and word not in string.punctuation and re.search('[a-zA-Z]', word)
]
freq_dist = FreqDist(word_list)
top_200 = freq_dist.most_common(200)
top_200 = [word[0] for word in top_200]
return top_200
def main():
sentences = get_all_book_sentences(books_dir)
sentences = [tokenize_nltk(s.lower()) for s in sentences]
#model = train_word2vec(sentences)
#test_model()
model = pickle.load(open(word2vec_model_path, 'rb'))
#accuracy_list = evaluate_model(model, evaluation_file)
#print(accuracy_list)
(analogy_score, word_list) = model.wv.evaluate_word_analogies(
datapath('questions-words.txt'))
print(analogy_score)
pretrained_model = KeyedVectors.load_word2vec_format(pretrained_model_path,
binary=True)
(analogy_score, word_list) = pretrained_model.evaluate_word_analogies(
datapath('questions-words.txt'))
print(analogy_score)
def create_vectorizers(data_dict):
topic_list = list(data_dict.keys())
vectorizer_dict = {}
for topic in topic_list:
text_array = data_dict[topic]
text = " ".join(text_array)
word_list = tokenize_nltk(text)
word_list = [word for word in word_list if word not in stopwords]
freq_dist = FreqDist(word_list)
top_200 = freq_dist.most_common(200)
vocab = [
wtuple[0] for wtuple in top_200 if wtuple[0] not in stopwords
and wtuple[0] not in string.punctuation
]
vectorizer_dict[topic] = CountVectorizer(vocabulary=vocab)
return vectorizer_dict
def get_stats(text, num_words=200):
word_list = tokenize_nltk(text)
word_list = [word for word in word_list if word not in stopwords and re.search("[A-Za-z]", word)]
freq_dist = FreqDist(word_list)
print(freq_dist.most_common(num_words))
return freq_dist
def pos_tag_nltk(text):
words = tokenize_nltk(text)
words_with_pos = nltk.pos_tag(words)
return words_with_pos