一般的经验法则是创建跨不同主题编号的 LDA 模型，然后检查 Jaccard similarity 和每个主题的连贯性。在这种情况下，连贯性通过主题中高分词之间的语义相似度来衡量单个主题（这些词是否在文本语料库中同时出现）。以下将对最佳主题数量给出强烈的直觉。这应该是跳转到分层 Dirichlet 过程之前的基线，因为已发现该技术在实际应用中存在问题。
首先为您要考虑的各种主题编号创建模型和主题词的字典，在这种情况下，corpus 是已清理的标记，num_topics 是您要考虑的主题列表，@987654328 @ 是您希望在指标中考虑的每个主题的热门词数：
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from gensim.models import LdaModel, CoherenceModel
from gensim import corpora
dirichlet_dict = corpora.Dictionary(corpus)
bow_corpus = [dirichlet_dict.doc2bow(text) for text in corpus]
# Considering 1-15 topics, as the last is cut off
num_topics = list(range(16)[1:])
num_keywords = 15
LDA_models = {}
LDA_topics = {}
for i in num_topics:
LDA_models[i] = LdaModel(corpus=bow_corpus,
id2word=dirichlet_dict,
num_topics=i,
update_every=1,
chunksize=len(bow_corpus),
passes=20,
alpha='auto',
random_state=42)
shown_topics = LDA_models[i].show_topics(num_topics=i,
num_words=num_keywords,
formatted=False)
LDA_topics[i] = [[word[0] for word in topic[1]] for topic in shown_topics]
现在创建一个函数来导出两个主题的 Jaccard 相似度：
def jaccard_similarity(topic_1, topic_2):
"""
Derives the Jaccard similarity of two topics
Jaccard similarity:
- A statistic used for comparing the similarity and diversity of sample sets
- J(A,B) = (A ∩ B)/(A ∪ B)
- Goal is low Jaccard scores for coverage of the diverse elements
"""
intersection = set(topic_1).intersection(set(topic_2))
union = set(topic_1).union(set(topic_2))
return float(len(intersection))/float(len(union))
通过考虑下一个主题，使用上述推导跨主题的平均稳定性：
LDA_stability = {}
for i in range(0, len(num_topics)-1):
jaccard_sims = []
for t1, topic1 in enumerate(LDA_topics[num_topics[i]]): # pylint: disable=unused-variable
sims = []
for t2, topic2 in enumerate(LDA_topics[num_topics[i+1]]): # pylint: disable=unused-variable
sims.append(jaccard_similarity(topic1, topic2))
jaccard_sims.append(sims)
LDA_stability[num_topics[i]] = jaccard_sims
mean_stabilities = [np.array(LDA_stability[i]).mean() for i in num_topics[:-1]]
gensim 有一个用于topic coherence 的内置模型（这使用'c_v' 选项）：
coherences = [CoherenceModel(model=LDA_models[i], texts=corpus, dictionary=dirichlet_dict, coherence='c_v').get_coherence()\
for i in num_topics[:-1]]
从这里大致通过每个主题数量的连贯性和稳定性之间的差异得出理想的主题数量：
coh_sta_diffs = [coherences[i] - mean_stabilities[i] for i in range(num_keywords)[:-1]] # limit topic numbers to the number of keywords
coh_sta_max = max(coh_sta_diffs)
coh_sta_max_idxs = [i for i, j in enumerate(coh_sta_diffs) if j == coh_sta_max]
ideal_topic_num_index = coh_sta_max_idxs[0] # choose less topics in case there's more than one max
ideal_topic_num = num_topics[ideal_topic_num_index]
最后将这些指标跨主题编号绘制成图表：
plt.figure(figsize=(20,10))
ax = sns.lineplot(x=num_topics[:-1], y=mean_stabilities, label='Average Topic Overlap')
ax = sns.lineplot(x=num_topics[:-1], y=coherences, label='Topic Coherence')
ax.axvline(x=ideal_topic_num, label='Ideal Number of Topics', color='black')
ax.axvspan(xmin=ideal_topic_num - 1, xmax=ideal_topic_num + 1, alpha=0.5, facecolor='grey')
y_max = max(max(mean_stabilities), max(coherences)) + (0.10 * max(max(mean_stabilities), max(coherences)))
ax.set_ylim([0, y_max])
ax.set_xlim([1, num_topics[-1]-1])
ax.axes.set_title('Model Metrics per Number of Topics', fontsize=25)
ax.set_ylabel('Metric Level', fontsize=20)
ax.set_xlabel('Number of Topics', fontsize=20)
plt.legend(fontsize=20)
plt.show()
您的理想主题数量将根据 Jaccard 相似性最大限度地提高连贯性并最大限度地减少主题重叠。在这种情况下，我们似乎可以安全地选择 14 左右的主题编号。