Análisis de texto Python (NLTK, jieba, snownlp)

El procesamiento del lenguaje natural (PNL) es el estudio de varias teorías y métodos que pueden lograr una comunicación efectiva entre humanos y computadoras utilizando el lenguaje natural, y también es una de las direcciones más importantes y difíciles en el campo de la inteligencia artificial. Es importante porque su teoría y práctica están estrechamente relacionadas con la exploración de los mecanismos espirituales del pensamiento, la cognición y la conciencia de los seres humanos; es difícil porque cada avance importante lleva diez años o más, y lleva mucho tiempo. esfuerzos de generaciones.
En los últimos años, la PNL ha logrado grandes avances en la segmentación de palabras en chino, el etiquetado de partes del discurso, la semántica léxica y el análisis sintáctico. Un gran número de tecnologías han sido aplicadas a la práctica comercial, y han obtenido buenos beneficios económicos y de mercado en el campo comercial. En términos de texto, existen principalmente motores de búsqueda inteligentes y recuperación inteligente basada en la comprensión del lenguaje natural, traducción automática inteligente, resumen automático y síntesis de texto, clasificación de texto y clasificación de documentos, sistema de marcado automático, filtrado de información y procesamiento de spam, investigación literaria y antigüedad. Investigación en chino, Corrección de gramática, minería de datos de texto y toma de decisiones inteligente, programación informática basada en lenguaje natural, etc. En cuanto a la voz, existen principalmente interpretación simultánea por máquina, atención al cliente inteligente, robot de chat, minería de voz y minería multimedia, extracción de información multimedia y conversión de texto, etc.

Instale la biblioteca NLTK

pip install NLTK

Instale el corpus
NLTK corpus
El período de validez de este enlace es permanente. Puede descargar directamente la carpeta nltk_data. Lo puse directamente debajo de la unidad D.
Los siguientes enlaces son los dos archivos txt necesarios.
Las palabras vacías que se usarán en el siguiente código y un archivo txt novedoso

segmentación de texto

import nltk.tokenize as tk
doc = "Are you curious about tokenization? " \
      "Let's see how it works! " \
      "We need to analyze a couple of sentences " \
      "with punctuations to see it in action."
tokens = tk.sent_tokenize(doc)  # 句子分词
for i, token in enumerate(tokens):
    print("%2d" % (i + 1), token)

import nltk.tokenize as tk
doc = "Are you curious about tokenization? " \
      "Let's see how it works! " \
      "We need to analyze a couple of sentences " \
      "with punctuations to see it in action."
tokens = tk.word_tokenize(doc)  # 单词分词
for i, token in enumerate(tokens):
    print("%2d" % (i + 1), token)

1 Are
 2 you
 3 curious
 4 about
 5 tokenization
 6 ?
 7 Let
 8 's
 9 see
10 how
11 it
12 works
13 !
14 We
15 need
16 to
17 analyze
18 a
19 couple
20 of
21 sentences
22 with
23 punctuations
24 to
25 see
26 it
27 in
28 action
29 .

derivación

import nltk.stem.porter as pt
import nltk.stem.lancaster as lc
import nltk.stem.snowball as sb

words = ['table', 'probably', 'wolves', 'playing', 'is',
         'dog', 'the', 'beaches', 'grounded', 'dreamt', 'envision']
pt_stemmer = pt.PorterStemmer()         # 波特词干提取器，偏宽松
lc_stemmer = lc.LancasterStemmer()      # 朗卡斯特词干提取器，偏严格
sb_stemmer = sb.SnowballStemmer('english')  # 思诺博词干提取器，偏中庸
for word in words:
    pt_stem = pt_stemmer.stem(word)
    lc_stem = lc_stemmer.stem(word)
    sb_stem = sb_stemmer.stem(word)
    print('%8s %8s %8s %8s' % (word, pt_stem, lc_stem, sb_stem))

   table     tabl     tabl     tabl
probably  probabl     prob  probabl
  wolves     wolv     wolv     wolv
 playing     play     play     play
      is       is       is       is
     dog      dog      dog      dog
     the      the      the      the
 beaches    beach    beach    beach
grounded   ground   ground   ground
  dreamt   dreamt   dreamt   dreamt
envision    envis    envid    envis

parte de la restauración del habla

import nltk.stem as ns
words = ['table', 'probably', 'wolves', 'playing',
         'is', 'dog', 'the', 'beaches', 'grounded',
         'dreamt', 'envision']
# 获取词性还原器对象
lemmatizer = ns.WordNetLemmatizer()
for word in words:
    n_lemma = lemmatizer.lemmatize(word, pos='n')   # 名词 词性还原
    v_lemma = lemmatizer.lemmatize(word, pos='v')   # 动词 词性还原
    print('%8s %8s %8s' % (word, n_lemma, v_lemma))

   table    table    table
probably probably probably
  wolves     wolf   wolves
 playing  playing     play
      is       is       be
     dog      dog      dog
     the      the      the
 beaches    beach    beach
grounded grounded   ground
  dreamt   dreamt    dream
envision envision envision

modelo bolsa de palabras

La semántica de una oración depende en gran medida de la cantidad de veces que aparece una palabra. El modelo de bolsa de palabras toma cada oración como muestra, y el modelo matemático construido con nombres de características y valores de evidencia especiales se denomina "bolsa de palabras". modelo de palabras".

Nombre especial: todas las palabras posibles en la oración
Valor especial: el número de veces que aparece la palabra en la oración

import nltk.tokenize as tk
import sklearn.feature_extraction.text as ft

doc = 'The brown dog is running. ' \
      'The black dog is in the black room. ' \
      'Running in the room is forbidden.'

# 对doc按照句子进行拆分
sents = tk.sent_tokenize(doc)

cv = ft.CountVectorizer()           # 构建词袋模型
bow = cv.fit_transform(sents)       # 训练词袋模型
print(cv.get_feature_names())       # 获取所有特征名
# ['black', 'brown', 'dog', 'forbidden', 'in', 'is', 'room', 'running', 'the']
print(bow.toarray())

['black', 'brown', 'dog', 'forbidden', 'in', 'is', 'room', 'running', 'the']
[[0 1 1 0 0 1 0 1 1]
 [2 0 1 0 1 1 1 0 2]
 [0 0 0 1 1 1 1 1 1]]

estadísticas de frecuencia de palabras

import nltk
doc = "who are you?where are you from?"
word_list = nltk.word_tokenize(doc)#英文分词
freq_list=nltk.FreqDist(doc)#统计词频
print(word_list)
for k,v in freq_list.items():
      print(k,v)

['who', 'are', 'you', '?', 'where', 'are', 'you', 'from', '?']
w 2
h 2
o 4
  5
a 2
r 4
e 4
y 2
u 2
? 2
f 1
m 1

from nltk.corpus import brown#导入brown语料库
from collections import Counter
print(brown.words())
# [u'The', u'Fulton', u'County', u'Grand', u'Jury', ...]
wordcounts = Counter(brown.words())#计数(区分大小写)
print(wordcounts['the'])
# 62713
print(wordcounts['The'])
# 7258
wordcounts_lower = Counter(i.lower() for i in brown.words())
#全部转换为小写
print(wordcounts_lower['The'])
# 0
print(wordcounts_lower['the'])
# 69971=62713+7258

['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]
62713
7258
0
69971

análisis de emociones

import nltk.corpus as nc
import nltk.classify as cf
import nltk.classify.util as cu

# 存储所有的正向样本
# pdata: [({单词:true}, 'pos'),(),()...]
pdata = []
# pos文件夹中的每个文件的路径
fileids = nc.movie_reviews.fileids('pos')
# print(len(fileids))
# 整理所有正面评论单词，存入pdata列表
for fileid in fileids:
    sample = {
    
    }
    # words: 把当前文档分词处理
    words = nc.movie_reviews.words(fileid)
    for word in words:
        sample[word] = True
    pdata.append((sample, 'POSITIVE'))
# 整理所有反向样本，存入ndata列表
ndata = []
fileids = nc.movie_reviews.fileids('neg')
for fileid in fileids:
    sample = {
    
    }
    words = nc.movie_reviews.words(fileid)
    for word in words:
        sample[word] = True
    ndata.append((sample, 'NEGATIVE'))

# 拆分测试集与训练集数量（80%作为训练集）
pnumb, nnumb = int(0.8 * len(pdata)), int(0.8 * len(ndata))
train_data = pdata[:pnumb] + ndata[:nnumb]
test_data = pdata[pnumb:] + ndata[nnumb:]
# 基于朴素贝叶斯分类器训练测试数据
model = cf.NaiveBayesClassifier.train(train_data)
ac = cu.accuracy(model, test_data)
print(ac)
#
# 模拟业务场景
reviews = [
    'It is an amazing movie.',
    'This is a dull movie. I would never recommend it to anyone.',
    'The cinematography is pretty great in this movie.',
    'The direction was terrible and the story was all over the place.']
for review in reviews:
    sample = {
    
    }
    words = review.split()
    for word in words:
        sample[word] = True
    pcls = model.classify(sample)
    print(review, '->', pcls)

0.735
It is an amazing movie. -> POSITIVE
This is a dull movie. I would never recommend it to anyone. -> NEGATIVE
The cinematography is pretty great in this movie. -> POSITIVE
The direction was terrible and the story was all over the place. -> NEGATIVE

Segmentación de palabras en chino jieba

pip install jieba

import jieba
doc="我喜欢静谧的环境，它能让人置身思考之中，" \
    "同繁华城市的夜景一样能启发人，在不喧哗的时候看城市也别有一番韵味" \
    "，但寻常人是受不了那份寂寞的。月光下，" \
    "忙碌一天的人们享受起生活，有人掀开扉页，" \
    "有人悠扬起曼妙的乐曲，有人漫步在白天忙碌奔走的街道上；" \
    "但同时，有人没有放松紧绷的发条，他们有为了生活卖大力气的，" \
    "有为了光辉的未来埋头苦读的，还有掩着头哭泣的，" \
    "在他们的眼中，月光是不一样的颜色。"
words_list=jieba.lcut(doc)
print(words_list)

Building prefix dict from the default dictionary ...
Dumping model to file cache C:\Users\dell\AppData\Local\Temp\jieba.cache
Loading model cost 0.604 seconds.
Prefix dict has been built successfully.
['我', '喜欢', '静谧', '的', '环境', '，', '它', '能', '让', '人', '置身', '思考', '之中', '，', '同', '繁华', '城市', '的', '夜景', '一样', '能', '启发', '人', '，', '在', '不', '喧哗', '的', '时候', '看', '城市', '也', '别', '有', '一番', '韵味', '，', '但', '寻常', '人', '是', '受不了', '那', '份', '寂寞', '的', '。', '月光', '下', '，', '忙碌', '一天', '的', '人们', '享受', '起', '生活', '，', '有人', '掀开', '扉页', '，', '有人', '悠扬', '起', '曼妙', '的', '乐曲', '，', '有人', '漫步', '在', '白天', '忙碌', '奔走', '的', '街道', '上', '；', '但', '同时', '，', '有人', '没有', '放松', '紧绷', '的', '发条', '，', '他们', '有', '为了', '生活', '卖大', '力气', '的', '，', '有', '为了', '光辉', '的', '未来', '埋头', '苦读', '的', '，', '还有', '掩着', '头', '哭泣', '的', '，', '在', '他们', '的', '眼中', '，', '月光', '是', '不', '一样', '的', '颜色', '。']

import jieba
str="东汉末年分三国当时有三个国家"
print(jieba.lcut(str))
print(jieba.lcut(str,cut_all=True))
print(jieba.lcut_for_search(str))

Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\dell\AppData\Local\Temp\jieba.cache
Loading model cost 0.790 seconds.
Prefix dict has been built successfully.
['东汉', '末年', '分', '三国', '当时', '有', '三个', '国家']
['东汉', '汉末', '末年', '分', '三国', '当时', '有', '三个', '国家']
['东汉', '末年', '分', '三国', '当时', '有', '三个', '国家']

import jieba
doc="这是南宫峻熙的博客"
print(jieba.lcut(doc))
jieba.add_word("南宫峻熙")
# 将指定词语加入到分词库
print(jieba.lcut(doc))

['这是', '南宫', '峻熙', '的', '博客']
['这是', '南宫峻熙', '的', '博客']

Estadísticas de frecuencia de palabras para el libro electrónico "Mi nuevo imperio de la dinastía Ming"

import jieba
from collections import Counter
def stopwordslist(filepath):# 创建停用词list
    stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()]
    return stopwords
def seg_sentence(sentence):# 对句子进行分词
    sentence_seged = jieba.cut(sentence.strip())
    stopwords = stopwordslist('stoplist.txt')  # 这里加载停用词的路径
    outstr = []
    for word in sentence_seged:
        if word not in stopwords:
            if word != '\t':
                outstr.append(word)
    return outstr
def word_frequency(line_seg):# 对分词进行词频展示
    c = Counter()
    for x in line_seg:
        if len(x) > 1 and x != '\r\n':
            c[x] += 1
    for (k, v) in c.most_common():
        print('%s%s  %d' % (' ' * (5 - len(k)), k, v))
inputs = open('我的大明新帝国.txt', 'r', encoding='utf-8')
lines = ""
for line in inputs:
    lines += line.replace("\n", "")
inputs.close()
line_seg = seg_sentence(lines)  # 这里的返回值是列表
print(word_frequency(line_seg))  # 取词频

Puede tardar unos minutos en ejecutarse, tenga paciencia

biblioteca snownlp

pip install snownlp

from snownlp import SnowNLP
doc="我喜欢静谧的环境，它能让人置身思考之中，" \
    "同繁华城市的夜景一样能启发人，在不喧哗的时候看城市也别有一番韵味" \
    "，但寻常人是受不了那份寂寞的。月光下，" \
    "忙碌一天的人们享受起生活，有人掀开扉页，" \
    "有人悠扬起曼妙的乐曲，有人漫步在白天忙碌奔走的街道上；" \
    "但同时，有人没有放松紧绷的发条，他们有为了生活卖大力气的，" \
    "有为了光辉的未来埋头苦读的，还有掩着头哭泣的，" \
    "在他们的眼中，月光是不一样的颜色。"
s=SnowNLP(doc)
print('词语:',s.words)
print('分句:',s.sentences)
print('情感偏向:',s.sentiments)
print('拼音:',s.pinyin)
print('关键字:',s.keywords(5))
print('摘要:',s.summary(5))
print('词频:',s.tf)
print('逆向文件频率:',s.idf)

词语: ['我', '喜欢', '静谧', '的', '环境', '，', '它', '能', '让', '人', '置身', '思考', '之中', '，', '同', '繁华', '城市', '的', '夜景', '一样', '能', '启发', '人', '，', '在', '不', '喧', '哗', '的', '时候', '看', '城市', '也', '别', '有', '一番', '韵味', '，', '但', '寻常', '人', '是', '受', '不', '了', '那', '份', '寂寞', '的', '。', '月光', '下', '，', '忙碌', '一', '天', '的', '人们', '享受', '起', '生活', '，', '有人', '掀开', '扉页', '，', '有人', '悠扬', '起曼', '妙', '的', '乐曲', '，', '有人', '漫步', '在', '白天', '忙碌', '奔走', '的', '街道', '上', '；', '但', '同时', '，', '有人', '没', '有', '放松', '紧', '绷', '的', '发', '条', '，', '他们', '有', '为了', '生活', '卖', '大', '力气', '的', '，', '有为', '了', '光辉', '的', '未来', '埋头苦', '读', '的', '，', '还有', '掩', '着头', '哭泣', '的', '，', '在', '他们', '的', '眼中', '，', '月光', '是', '不', '一样', '的', '颜色', '。']
分句: ['我喜欢静谧的环境', '它能让人置身思考之中', '同繁华城市的夜景一样能启发人', '在不喧哗的时候看城市也别有一番韵味', '但寻常人是受不了那份寂寞的', '月光下', '忙碌一天的人们享受起生活', '有人掀开扉页', '有人悠扬起曼妙的乐曲', '有人漫步在白天忙碌奔走的街道上', '但同时', '有人没有放松紧绷的发条', '他们有为了生活卖大力气的', '有为了光辉的未来埋头苦读的', '还有掩着头哭泣的', '在他们的眼中', '月光是不一样的颜色']
情感偏向: 0.9999999999999998
拼音: ['wo', 'xi', 'huan', 'jing', 'mi', 'de', 'huan', 'jing', '，', 'ta', 'neng', 'rang', 'ren', 'zhi', 'shen', 'si', 'kao', 'zhi', 'zhong', '，', 'tong', 'fan', 'hua', 'cheng', 'shi', 'de', 'ye', 'jing', 'yi', 'yang', 'neng', 'qi', 'fa', 'ren', '，', 'zai', 'bu', 'xuan', 'hua', 'de', 'shi', 'hou', 'kan', 'cheng', 'shi', 'ye', 'bie', 'you', 'yi', 'fan', 'yun', 'wei', '，', 'dan', 'xun', 'chang', 'ren', 'shi', 'shou', 'bu', 'liao', 'na', 'fen', 'ji', 'mo', 'de', '。', 'yue', 'guang', 'xia', '，', 'mang', 'lu', 'yi', 'tian', 'de', 'ren', 'men', 'xiang', 'shou', 'qi', 'sheng', 'huo', '，', 'you', 'ren', 'xian', 'kai', 'fei', 'ye', '，', 'you', 'ren', 'you', 'yang', 'qi', 'man', 'miao', 'de', 'yue', 'qu', '，', 'you', 'ren', 'man', 'bu', 'zai', 'bai', 'tian', 'mang', 'lu', 'ben', 'zou', 'de', 'jie', 'dao', 'shang', '；', 'dan', 'tong', 'shi', '，', 'you', 'ren', 'mei', 'you', 'fang', 'song', 'jin', 'beng', 'de', 'fa', 'tiao', '，', 'ta', 'men', 'you', 'wei', 'liao', 'sheng', 'huo', 'mai', 'da', 'li', 'qi', 'de', '，', 'you', 'wei', 'liao', 'guang', 'hui', 'de', 'wei', 'lai', 'mai', 'tou', 'ku', 'du', 'de', '，', 'hai', 'you', 'yan', 'zhe', 'tou', 'ku', 'qi', 'de', '，', 'zai', 'ta', 'men', 'de', 'yan', 'zhong', '，', 'yue', 'guang', 'shi', 'bu', 'yi', 'yang', 'de', 'yan', 'shai', '。']
关键字: ['有人', '人', '不', '忙碌', '城市']
摘要: ['月光是不一样的颜色', '有人漫步在白天忙碌奔走的街道上', '但寻常人是受不了那份寂寞的', '忙碌一天的人们享受起生活', '有人掀开扉页']
词频: [{
    
    '我': 1}, {
    
    '喜': 1}, {
    
    '欢': 1}, {
    
    '静': 1}, {
    
    '谧': 1}, {
    
    '的': 1}, {
    
    '环': 1}, {
    
    '境': 1}, {
    
    '，': 1}, {
    
    '它': 1}, {
    
    '能': 1}, {
    
    '让': 1}, {
    
    '人': 1}, {
    
    '置': 1}, {
    
    '身': 1}, {
    
    '思': 1}, {
    
    '考': 1}, {
    
    '之': 1}, {
    
    '中': 1}, {
    
    '，': 1}, {
    
    '同': 1}, {
    
    '繁': 1}, {
    
    '华': 1}, {
    
    '城': 1}, {
    
    '市': 1}, {
    
    '的': 1}, {
    
    '夜': 1}, {
    
    '景': 1}, {
    
    '一': 1}, {
    
    '样': 1}, {
    
    '能': 1}, {
    
    '启': 1}, {
    
    '发': 1}, {
    
    '人': 1}, {
    
    '，': 1}, {
    
    '在': 1}, {
    
    '不': 1}, {
    
    '喧': 1}, {
    
    '哗': 1}, {
    
    '的': 1}, {
    
    '时': 1}, {
    
    '候': 1}, {
    
    '看': 1}, {
    
    '城': 1}, {
    
    '市': 1}, {
    
    '也': 1}, {
    
    '别': 1}, {
    
    '有': 1}, {
    
    '一': 1}, {
    
    '番': 1}, {
    
    '韵': 1}, {
    
    '味': 1}, {
    
    '，': 1}, {
    
    '但': 1}, {
    
    '寻': 1}, {
    
    '常': 1}, {
    
    '人': 1}, {
    
    '是': 1}, {
    
    '受': 1}, {
    
    '不': 1}, {
    
    '了': 1}, {
    
    '那': 1}, {
    
    '份': 1}, {
    
    '寂': 1}, {
    
    '寞': 1}, {
    
    '的': 1}, {
    
    '。': 1}, {
    
    '月': 1}, {
    
    '光': 1}, {
    
    '下': 1}, {
    
    '，': 1}, {
    
    '忙': 1}, {
    
    '碌': 1}, {
    
    '一': 1}, {
    
    '天': 1}, {
    
    '的': 1}, {
    
    '人': 1}, {
    
    '们': 1}, {
    
    '享': 1}, {
    
    '受': 1}, {
    
    '起': 1}, {
    
    '生': 1}, {
    
    '活': 1}, {
    
    '，': 1}, {
    
    '有': 1}, {
    
    '人': 1}, {
    
    '掀': 1}, {
    
    '开': 1}, {
    
    '扉': 1}, {
    
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