Reference

Distributed Representations of Sentences and Documents

Library

!apt-get install libmecab-dev mecab mecab-ipadic-utf8
!apt-get -q -y install swig  
!pip install mecab-python3
!echo `mecab-config --dicdir`"/mecab-ipadic-neologd"

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

import MeCab

from tqdm import tqdm

from gensim.models.doc2vec import Doc2Vec, TaggedDocument

from sklearn.cluster import KMeans
from sklearn.decomposition import PCA

Data

df_news = pd.read_csv('/content/gdrive/My Drive/xxx/xxx.csv')

print ('shape of df_news: ', df_news.shape)
df_news.head()

df_g = df_news.groupby('NEWS_DATE')['NEWS_TITLE'].sum().reset_index()

print (df_g.shape)
df_g.head()

dates = df_g['NEWS_DATE']
titles = df_g['NEWS_TITLE']

idx = np.random.randint(len(dates))

txt = titles[idx]

tagger = MeCab.Tagger()
# tagger = MeCab.Tagger('-d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd')

tagger.parse('')

node = tagger.parseToNode(txt)
    
result = []
s1 = txt
flag = 0
while node:
  s2 = node.surface
  s3 = s1.replace(s2, '')
  hinshi = node.feature.split(',')[0]

  if flag == 1:
    result.append(s3)
    flag = 0
  if hinshi in ['名詞']:
    flag = 1
  elif hinshi in ['動詞', '形容詞']:
    result.append(node.feature.split(',')[6])
  else:
    pass

  s1 = s2
  node = node.next
  
print ('Date: ', dates[idx])  
print ('words: ', result)
print ('# of words: ', len(result))

Doc2Vec

%%time

tagged_docs = []

for date, title in tqdm(zip(dates, titles)):
  tagger = MeCab.Tagger()
  tagger.parse('')

  node = tagger.parseToNode(title)

  words = []
  s1 = title
  flag = 0
  while node:
    s2 = node.surface
    s3 = s1.replace(s2, '')
    hinshi = node.feature.split(',')[0]

    if flag == 1:
      words.append(s3)
      flag = 0
    if hinshi in ['名詞']:
      flag = 1
    elif hinshi in ['動詞', '形容詞']:
      words.append(node.feature.split(',')[6])
    else:
      pass

    s1 = s2
    node = node.next
    
  tagged_docs.append(TaggedDocument(words=words, tags=[date]))

%%time

vec_size = 50
alpha = 0.025
alpha_delta = 0.0002
max_epochs = 20

model = Doc2Vec(vector_size=vec_size, alpha=alpha, min_alpha=alpha, min_count=1, dm =1) 
model.build_vocab(tagged_docs)

for epoch in range(max_epochs):
  model.train(tagged_docs, total_examples=model.corpus_count, 
              epochs=model.iter)
  model.alpha -= alpha_delta
  model.min_alpha = model.alpha

idx = np.random.randint(len(tagged_docs))

print ('Date: ', dates[idx])
print ('similar dates: ', model.docvecs.most_similar(dates[idx], topn=3))

date1 = '2018-06-07'
date2 = '2018-08-29'

print (df_news[df_news['NEWS_DATE']==date1].sort_values('NEWS_TIME'))
print ()
print (df_news[df_news['NEWS_DATE']==date2].sort_values('NEWS_TIME'))

# save and load model
from gensim.test.utils import get_tmpfile

path = '/content/gdrive/My Drive/xxx/'

fname = get_tmpfile(path+'doc2vec_model')
model.save(fname)

loaded_model = Doc2Vec.load(fname)

# no more updates
model.delete_temporary_training_data(keep_doctags_vectors=True, keep_inference=True)

# infer vector for a new document
vector = model.infer_vector(['米ダウ平均', '上昇'])

print (vector[:10])

Clustering

doc_vectors = model.docvecs.vectors_docs

print ('shape of doc_vectors: ', doc_vectors.shape)
# print (doc_vectors[0])

kmeans_model = KMeans(n_clusters=3, init='k-means++', max_iter=100)  
kmeans_model.fit(doc_vectors)

labels=kmeans_model.labels_.tolist()

pca = PCA(n_components=2).fit(doc_vectors)
datapoint = pca.transform(doc_vectors)

plt.figure(figsize=(5, 3))
# label1 = ["#FFFF00", "#008000", "#0000FF", "#800080"]
label1 = ["#FFFF00", "#008000", "#0000FF"]

color = [label1[i] for i in labels]

plt.scatter(datapoint[:, 0], datapoint[:, 1], c=color)

centroids = kmeans_model.cluster_centers_
centroidpoint = pca.transform(centroids)
plt.scatter(centroidpoint[:, 0], centroidpoint[:, 1], marker='^', s=150, c='#000000')
plt.show()

Word2Vec

# model = gensim.models.Word2Vec.load(path + 'ja.bin')
model = gensim.models.Word2Vec.load(path + 'jv.bin')

# model.wv['king']
model.wv.most_similar('king')
# model.wv.most_similar(positive=['woman', 'king'], negative=['man'])