Python_文本分析_困惑度计算和一致性检验

在做LDA的过程中比较比较难的问题就是主题数的确定，下面介绍困惑度、一致性这两种方法的实现。

其中的一些LDA的参数需要结合自己的实际进行设定 直接计算出的log_perplexity是负值，是困惑度经过对数去相反数得到的。

import csv
import datetime
import re
import pandas as pd
import numpy as np
import jieba
import matplotlib.pyplot as plt
import jieba.posseg as jp, jieba
import gensim
from snownlp import seg
from snownlp import SnowNLP
from snownlp import sentiment
from gensim import corpora, models
from gensim.models import CoherenceModel
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF, LatentDirichletAllocation
import warnings
warnings.filterwarnings("ignore")

comment = pd.read_csv(r"good_1", header = 0, index_col = False, engine=python,encoding = utf-8)
csv_data = comment[[(len(str(x)) > 100) for x in comment[segment]]]
print(csv_data.shape)

# 构造corpus
train = []
for i in range(csv_data.shape[0]):
    comment = csv_data.iloc[i,7].split()
    train.append(comment)
    
id2word = corpora.Dictionary(train)
corpus = [ id2word.doc2bow(sentence) for sentence in train]

# 一致性和困惑度计算
coherence_values = []
perplexity_values = []
model_list = []

for topic in range(15):
    lda_model = gensim.models.LdaMulticore(corpus = corpus, num_topics=topic+1, id2word = id2word, random_state=100, chunksize=100, passes=10, per_word_topics=True)
    perplexity = pow(2,-lda_model.log_perplexity(corpus)) 
    print(perplexity,end=   )
    perplexity_values.append(round(perplexity,3))
    
    model_list.append(lda_model)
    coherencemodel = CoherenceModel(model=lda_model, texts=train, dictionary=id2word, coherence=c_v)
    coherence_values.append(round(coherencemodel.get_coherence(),3))

下面展示一种一致性可视化的方法

x = range(1,21)
plt.plot(x, coherence_values)
plt.xlabel("Num Topics")
plt.ylabel("Coherence score")
plt.legend(("coherence_values"), loc=best)
plt.show()