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如何.txt在 LDA 模型中动态传递用户给出的文档？我已经尝试了下面的代码，但它不能给出正确的文档主题。我的主题.txt与体育有关，因此应将主题名称命名为体育。它给出的输出为：

Score: 0.5569453835487366   - Topic: 0.008*"bike" + 0.005*"game" + 0.005*"team" + 0.004*"run" + 0.004*"virginia"
Score: 0.370819091796875    - Topic: 0.016*"game" + 0.014*"team" + 0.011*"play" + 0.008*"hockey" + 0.008*"player"
Score: 0.061239391565322876  -Topic: 0.010*"card" + 0.010*"window" + 0.008*"driver" + 0.007*"sale" + 0.006*"price"*

data = df.content.values.tolist()
data = [re.sub('\S*@\S*\s?', '', sent) for sent in data]
data = [re.sub('\s+', ' ', sent) for sent in data]
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))
bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.
trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  
bigram_mod = gensim.models.phrases.Phraser(bigram)
trigram_mod = gensim.models.phrases.Phraser(trigram)
def remove_stopwords(texts):
    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] 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']):

    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)
data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])

id2word = gensim.corpora.Dictionary(data_lemmatized)

texts = data_lemmatized

corpus = [id2word.doc2bow(text) for text in texts]
# Build LDA model
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                           id2word=id2word,
                                           num_topics=20, 
                                           random_state=100,
                                           update_every=1,
                                           chunksize=100,
                                           passes=10,
                                           alpha='auto',
                                           per_word_topics=True)

#f = io.open("text.txt", mode="r", encoding="utf-8")

p=open("text.txt", "r") #document by the user which is related to sports

if p.mode == 'r':
    content = p.read()

bow_vector = id2word.doc2bow(lemmatization(p))

for index, score in sorted(lda_model[bow_vector], key=lambda tup: -1*tup[1]):
    print("Score: {}\t Topic: {}".format(score, lda_model.print_topic(index, 5)))
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2 回答 2

1

经过一番尝试，这对我有用，如果您有不同的意见，请发表评论。

bow_vector = dictionary.doc2bow(preprocess(content))
q= lda_model[bow_vector]

from operator import itemgetter 
res = max(q, key = itemgetter(1))[0] 
res1 = max(q, key = itemgetter(1))[1] 

if (res  == 1 ):
    print("This .txt file is related to Politics/Government, Accuracy:",res1)
elif (res == 2) :
        print("This .txt file is related to sports, Accuracy:",res1)
elif res==3:
        print("This .txt file is related to Computer, Accuracy:",res1)
elif..... (so on)
 else.
于 2020-03-31T11:32:34.673 回答
1

你所有的代码都是正确的，但我认为你对 LDA 建模的期望可能有点偏离。您收到的输出是正确的！

首先，您使用了短语“主题名称”；LDA 生成的主题没有名称，并且它们没有与用于训练模型的数据标签的简单映射。这是一个无监督模型，通常你会使用没有标签的数据来训练 LDA。如果您的语料库包含属于 A、B、C、D 类的文档，并且您训练 LDA 模型以输出四个主题 L、M、N、O，则不存在如下映射：

A -> M
B -> L
C -> O
D -> N

其次，要注意输出中标记和主题之间的差异。LDA 的输出类似于：

主题 1：0.5 - 0.005*"token_13" + 0.003*"token_204" + ...

主题 2：0.07 - 0.01*"token_24" + 0.001*"token_3" + ...

换句话说，每个文档都有属于每个主题的概率。并且每个主题都由以某种方式加权的每个语料库标记的总和组成，以唯一地定义主题。

很容易查看每个主题中权重最大的标记并将主题解释为一个类。例如：

# If you have:
topic_1 = 0.1*"dog" + 0.08*"cat" + 0.04*"snake"

# It's tempting to name topic_1 = pets

但这很难验证，并且严重依赖人类的直觉。LDA 更常见的用法是当您没有标签时，您想识别哪些文档在语义上彼此相似，而不必确定文档的正确类标签是什么。

于 2020-03-26T14:04:25.203 回答