Python機(jī)器學(xué)習(xí)NLP自然語(yǔ)言處理Word2vec電影影評(píng)建模
更新時(shí)間：2021年09月21日 13:55:06 作者：我是小白呀
本文是Python機(jī)器學(xué)習(xí)NLP自然語(yǔ)言處理系列文章，帶大家開(kāi)啟一段學(xué)習(xí)自然語(yǔ)言處理 (NLP) 的旅程. 本篇文章主要學(xué)習(xí)NLP自然語(yǔ)言處理基本操作Word2vec電影影評(píng)建模
概述
從今天開(kāi)始我們將開(kāi)啟一段自然語(yǔ)言處理 (NLP) 的旅程. 自然語(yǔ)言處理可以讓來(lái)處理, 理解, 以及運(yùn)用人類的語(yǔ)言, 實(shí)現(xiàn)機(jī)器語(yǔ)言和人類語(yǔ)言之間的溝通橋梁.
詞向量
我們先來(lái)說(shuō)說(shuō)詞向量究竟是什么. 當(dāng)我們把文本交給算法來(lái)處理的時(shí)候, 計(jì)算機(jī)并不能理解我們輸入的文本, 詞向量就由此而生了. 簡(jiǎn)單的來(lái)說(shuō), 詞向量就是將詞語(yǔ)轉(zhuǎn)換成數(shù)字組成的向量.
當(dāng)我們描述一個(gè)人的時(shí)候, 我們會(huì)使用身高體重等種種指標(biāo), 這些指標(biāo)就可以當(dāng)做向量. 有了向量我們就可以使用不同方法來(lái)計(jì)算相似度.
那我們?nèi)绾蝸?lái)描述語(yǔ)言的特征呢? 我們把語(yǔ)言分割成一個(gè)個(gè)詞, 然后在詞的層面上構(gòu)建特征.
詞向量維度
詞向量的維度越高, 其所能提供的信息也就越多, 計(jì)算結(jié)果的可靠性就更值得信賴.
50 維的詞向量:
用熱度圖表示一下:
從上圖我們可以看出, 相似的詞在特征表達(dá)中比較相似. 由此也可以證明詞的特征是有意義的.
代碼實(shí)現(xiàn)
預(yù)處理
import numpy as np
import pandas as pd
import itertools
import re
from bs4 import BeautifulSoup
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from matplotlib import pyplot as plt
import nltk
# 停用詞
stop_words = pd.read_csv("data/stopwords.txt", index_col=False, quoting=3, sep="\n", names=["stop_words"])
stop_words = [word.strip() for word in stop_words["stop_words"].values]
def load_train_data():
"""讀取訓(xùn)練數(shù)據(jù)"""
# 語(yǔ)料
data = pd.read_csv("data/labeledTrainData.tsv", sep="\t", escapechar="\\")
print(data[:5])
print("訓(xùn)練評(píng)論數(shù)量:", len(data)) # 25,000
return data
def load_test_data():
# 語(yǔ)料
data = pd.read_csv("data/unlabeledTrainData.tsv", sep="\t", escapechar="\\")
print("測(cè)試評(píng)論數(shù)量:", len(data)) # 50,000
return data
def pre_process(text):
# 去除網(wǎng)頁(yè)鏈接
text = BeautifulSoup(text, "html.parser").get_text()
# 去除標(biāo)點(diǎn)
text = re.sub("[^a-zA-Z]", " ", text)
# 分詞
words = text.lower().split()
# 去除停用詞
words = [w for w in words if w not in stop_words]
return " ".join(words)
def split_train_data():
# 讀取文件
data = pd.read_csv("data/train.csv")
print(data.head())
# 抽取bag of words特征
vec = CountVectorizer(max_features=5000)
# 擬合
vec.fit(data["review"])
# 轉(zhuǎn)換
train_data_features = vec.transform(data["review"]).toarray()
print(train_data_features.shape)
# 詞袋
print(vec.get_feature_names())
# 分割數(shù)據(jù)集
X_train, X_test, y_train, y_test = train_test_split(train_data_features, data["sentiment"], test_size=0.2,
random_state=0)
return X_train, X_test, y_train, y_test
def test():
# 讀取測(cè)試數(shù)據(jù)
data = pd.read_csv("data/test.csv")
print(data.head())
tokenizer = nltk.data.load("tokenizers/punkt/english.pickle")
# 分詞
def split_sentences(review):
raw_sentences = tokenizer.tokenize(review.strip())
return sentences
sentences = sum(data["review"][:10].apply(split_sentences), [])
def visualize(cm, classes, title="Confusion matrix", cmap=plt.cm.Blues):
plt.imshow(cm, interpolation="nearest", cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=0)
plt.yticks(tick_marks, classes)
thresh = cm.max()
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel("True label")
plt.xlabel("Predicted label")
plt.show()
if __name__ == '__main__':
# # 處理訓(xùn)練數(shù)據(jù)
# train_data = load_train_data()
# train_data["review"] = train_data["review"].apply(pre_process)
# print(train_data.head())
#
# # 保存
# train_data.to_csv("data/train.csv")
# # 處理訓(xùn)練數(shù)據(jù)
# test_data = load_test_data()
# test_data["review"] = test_data["review"].apply(pre_process)
# print( test_data.head())
#
# # 保存
# test_data.to_csv("data/test.csv")
split_train_data()
主程序
import pandas as pd
import nltk
from gensim.models.word2vec import Word2Vec
def pre_process():
"""預(yù)處理"""
# 讀取測(cè)試數(shù)據(jù)
data = pd.read_csv("data/test.csv")
print(data.head())
# 存放結(jié)果
result = []
# 分詞
for line in data["review"]:
result.append(nltk.word_tokenize(line))
return result
def main():
# 獲取分詞語(yǔ)料
word_list = pre_process()
# 設(shè)定詞向量訓(xùn)練的參數(shù)
num_features = 300 # Word vector dimensionality
min_word_count = 40 # Minimum word count
num_workers = 4 # Number of threads to run in parallel
context = 10 # Context window size
model_name = '{}features_{}minwords_{}context.model'.format(num_features, min_word_count, context)
# 創(chuàng)建w2c模型
model = Word2Vec(sentences=word_list, workers=num_workers,
vector_size=num_features, min_count=min_word_count,
window=context)
# 保存模型
model.save(model_name)
def test():
# 加載模型
model = Word2Vec.load("300features_40minwords_10context.model")
# 不匹配
match = model.wv.doesnt_match(['man','woman','child','kitchen'])
print(match)
# 最相似
print(model.wv.most_similar("boy"))
print(model.wv.most_similar("bad"))
if __name__ == '__main__':
test()
輸出結(jié)果:
2021-09-16 20:36:40.791181: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
Unnamed: 0 id sentiment review
0 0 5814_8 1 stuff moment mj ve started listening music wat...
1 1 2381_9 1 classic war worlds timothy hines entertaining ...
2 2 7759_3 0 film starts manager nicholas bell investors ro...
3 3 3630_4 0 assumed praised film filmed opera didn read do...
4 4 9495_8 1 superbly trashy wondrously unpretentious explo...
73423
[[15958 623 12368 4459 622 835 30 152 2097 2408 35364 57143
892 2997 766 42223 967 266 25276 157 108 696 1631 198
2576 9850 3745 27 52 3789 9503 696 526 52 354 862
474 38 2 101 11027 696 6456 22390 969 5873 5376 4044
623 1401 2069 718 618 92 96 138 1345 714 96 18
123 1770 518 3314 354 983 1888 520 83 73 983 2
28 28635 1044 2054 401 1071 85 8565 8957 7226 804 46
224 447 2113 2691 5742 10 5 3217 943 5045 980 373
28 873 438 389 41 23 19 56 122 9 253 27176
2149 19 90 57144 53 4874 696 6558 136 2067 10682 48
518 1482 9 3668 1587 3786 2 110 10 506 25150 20744
340 33 316 17 4824 3892 978 14 10150 2596 766 42223
5082 4784 700 198 6276 5254 700 198 2334 696 20879 5
86 30 2 583 2872 30601 30 86 28 83 73 32
96 18 2 224 708 30 167 7 3791 216 45 513
2 2310 513 1860 4536 1925 414 1321 578 7434 851 696
997 5354 57145 162 30 2 91 1839]
[ 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 357 684
28 3027 10371 5801 20987 21481 19800 1 3027 10371 21481 19800
1719 204 49 168 250 7355 1547 374 401 5415 24 1719
24 49 168 7355 1547 3610 21481 19800 123 204 49 168
1102 1547 656 213 5432 5183 61 4 66166 20 36 56
7 5183 2025 116 5031 11 45 782]
[ 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 2189 1 586
2189 15 1855 615 400 5394 3797 23866 2892 481 2892 810
22020 17820 1 741 231 20 746 2028 1040 6089 816 5555
41772 1762 26 811 288 8 796 45]
[ 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 85 310 1734 78 1906 78 1906 1412 1985
78 7644 1412 244 9287 7092 6374 2584 6183 3795 3080 1288
2217 3534 6005 4851 1543 762 1797 26144 699 237 6745 7
1288 1415 9003 5623 237 1669 17987 874 421 234 1278 347
9287 1609 7100 1065 75 9800 3344 76 5021 47 380 3015
14366 6523 1396 851 22330 3465 20861 7106 6374 340 60 19035
3089 5081 3 7 1695 10735 3582 92 6374 176 8348 60
1491 11540 28826 1847 464 4099 22 3561 51 22 1538 1027
38926 2195 1966 3089 33 19894 287 142 6374 184 37 4025
67 325 37 421 549 21976 28 7744 2466 31533 27 2836
1339 6374 14805 1670 4666 60 33 12]
[ 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 27 52
4639 9 5774 1545 8575 855 10463 2688 21019 1542 1701 653
9765 9 189 706 2212 18342 566 437 2639 4311 4504 26110
307 496 893 317 1 27 52 587]]
[[0. 1.]
[0. 1.]
[0. 1.]
[1. 0.]
[0. 1.]]
2021-09-16 20:36:46.488438: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-09-16 20:36:46.489070: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: /usr/lib/x86_64-linux-gnu/libcuda.so.1: file too short; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu
2021-09-16 20:36:46.489097: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-09-16 20:36:46.489128: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (313c6f2d15e2): /proc/driver/nvidia/version does not exist
2021-09-16 20:36:46.489488: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX512F
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-09-16 20:36:46.493241: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, None, 200) 14684800
_________________________________________________________________
lstm (LSTM) (None, 200) 320800
_________________________________________________________________
dropout (Dropout) (None, 200) 0
_________________________________________________________________
dense (Dense) (None, 64) 12864
_________________________________________________________________
dense_1 (Dense) (None, 2) 130
=================================================================
Total params: 15,018,594
Trainable params: 15,018,594
Non-trainable params: 0
_________________________________________________________________
None
2021-09-16 20:36:46.792534: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-09-16 20:36:46.830442: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2300000000 Hz
Epoch 1/2
313/313 [==============================] - 101s 315ms/step - loss: 0.5581 - accuracy: 0.7229 - val_loss: 0.3703 - val_accuracy: 0.8486
Epoch 2/2
313/313 [==============================] - 98s 312ms/step - loss: 0.2174 - accuracy: 0.9195 - val_loss: 0.3016 - val_accuracy: 0.8822
以上就是Python機(jī)器學(xué)習(xí)NLP自然語(yǔ)言處理Word2vec電影影評(píng)建模的詳細(xì)內(nèi)容，更多關(guān)于NLP自然語(yǔ)言處理的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！
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