我正在使用gensim（在jupyter笔记本中）进行主题建模。我成功创建了一个模型并将其可视化。下面是代码：

import time
start_time = time.time()
import re
import spacy
import nltk
import pyLDAvis
import pyLDAvis.gensim
import gensim
import gensim.corpora as corpora
from gensim.utils import simple_preprocess
from gensim.models import CoherenceModel
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',level=logging.ERROR)
import warnings
warnings.filterwarnings("ignore",category=DeprecationWarning)
# nlp = spacy.load('en')
stop_word_list = nltk.corpus.stopwords.words('english')
stop_word_list.extend(['from','subject','re','edu','use'])
df = pd.read_csv('Topic_modeling.csv')
data = df.Articles.values.tolist()

# Remove Emails
data = [re.sub('\S*@\S*\s?','',sent) for sent in data]

# Remove new line characters
data = [re.sub('\s+',' ',sent) for sent in data]

# Remove distracting single quotes
data = [re.sub("\'","",sent) for sent in data]


def sent_to_words(sentences):
    for sentence in sentences:
        yield gensim.utils.simple_preprocess(str(sentence),deacc=True)  # deacc=True removes punctuations


data_words = list(sent_to_words(data))

# Build the bigram and trigram models
bigram = gensim.models.Phrases(data_words,min_count=5,threshold=100) # higher threshold fewer phrases.
trigram = gensim.models.Phrases(bigram[data_words],threshold=100)

# Faster way to get a sentence clubbed as a trigram/bigram
bigram_mod = gensim.models.phrases.Phraser(bigram)
trigram_mod = gensim.models.phrases.Phraser(trigram)

# Define functions for stopwords,bigrams,trigrams and lemmatization
def remove_stopwords(texts):
    return [[word for word in simple_preprocess(str(doc)) if word not in stop_word_list] for doc in texts]

def make_bigrams(texts):
    return [bigram_mod[doc] for doc in texts]

def make_trigrams(texts):
    return [trigram_mod[bigram_mod[doc]] for doc in texts]

def lemmatization(texts,allowed_postags=['NOUN','ADJ','VERB','ADV']):
    """https://spacy.io/api/annotation"""
    texts_out = []
    for sent in texts:
        doc = nlp(" ".join(sent))
        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])
    return texts_out


# Remove Stop Words
data_words_nostops = remove_stopwords(data_words)

# Form Bigrams
data_words_bigrams = make_bigrams(data_words_nostops)

# Initialize spacy 'en' model,keeping only tagger component (for efficiency)
# python3 -m spacy download en
nlp = spacy.load('en',disable=['parser','ner'])

# Do lemmatization keeping only noun,adj,vb,adv
data_lemmatized = lemmatization(data_words_bigrams,'ADJ'])

# Create Dictionary
id2word = corpora.Dictionary(data_lemmatized)

# Create Corpus
texts = data_lemmatized

# Term Document Frequency
corpus = [id2word.doc2bow(text) for text in texts]


# Build LDA model
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,id2word=id2word,num_topics= 3,random_state=100,update_every=1,chunksize=100,passes=20,alpha=0.4,eta=0.2,per_word_topics=True)

print(lda_model.print_topics())
doc_lda = lda_model[corpus]

现在，我想在每个句子中找到主要主题。所以我正在使用以下代码：

def format_topics_sentences(ldamodel=lda_model,corpus=corpus,texts=data):
    # Init output
    sent_topics_df = pd.DataFrame()

    # Get main topic in each document
    for i,row in enumerate(ldamodel[corpus]):
        row = sorted(row,key=lambda x: (x[1]),reverse=True)
        # Get the Dominant topic,Perc Contribution and Keywords for each document
        for j,(topic_num,prop_topic) in enumerate(row):
            if j == 0:  # => dominant topic
                wp = ldamodel.show_topic(topic_num)
                topic_keywords = ",".join([word for word,prop in wp])
                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num),round(prop_topic,4),topic_keywords]),ignore_index=True)
            else:
                break
    sent_topics_df.columns = ['Dominant_Topic','Perc_Contribution','Topic_Keywords']

    # Add original text to the end of the output
    contents = pd.Series(texts)
    sent_topics_df = pd.concat([sent_topics_df,contents],axis=1)
    return(sent_topics_df)


df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model,texts=data)

# Format
df_dominant_topic = df_topic_sents_keywords.reset_index()
df_dominant_topic.columns = ['Document_No','Dominant_Topic','Topic_Perc_Contrib','Keywords','Text']

# Show
df_dominant_topic.head(10)

但是，我遇到了以下错误：

  

TypeError跟踪（最近的呼叫   最后）        22        23   ---> 24 df_topic_sents_keywords = format_topics_sentences（ldamodel = lda_model，语料库=语料库，文本=数据）        25        26＃格式

     

以format_topics_sentences（ldamodel，   语料，文本）         5＃获取每个文档中的主要主题         对于i，在enumerate（ldamodel [corpus]）中的行为6：   ----> 7行=已排序（行，键= lambda x：（x [1]），reverse = True）         8＃获取每个文档的主要主题，Perc贡献和关键字         对于枚举（行）中的j，（topic_num，prop_topic）为9：

     

TypeError：“ int”和“ tuple”的实例之间不支持“

我不明白这是什么问题。 有人可以帮忙吗？

谢谢！