[英]LDA: topic model gensim gives same set of topics
Why am I getting same set of topics # words in gensim lda model?为什么我在 gensim lda model 中得到相同的主题集#字? I used these parameters.
我使用了这些参数。 I checked there are no duplicate documents in my corpus.
我检查了我的语料库中没有重复的文档。
lda_model = gensim.models.ldamodel.LdaModel(corpus=MY_CORPUS,
id2word=WORD_AND_ID,
num_topics=4,
minimum_probability=minimum_probability,
random_state=100,
update_every=1,
chunksize=100,
passes=10,
alpha='auto', # symmetric, asymmetric
per_word_topics=True)
[
(0, '0.004*lily + 0.01*rose + 0.00*jasmine'),
(1, '0.005*geometry + 0.07*algebra + 0.01*calculation'),
(2, '0.003*painting + 0.001*brush + 0.01*colors'),
(3, '0.005*geometry + 0.07*algebra + 0.01*calculation')
]
Notice: Topic #1 and #3 are identical.注意:主题#1 和#3 相同。
Each of the topics likely contains a large number of words weighted differently.每个主题都可能包含大量不同权重的单词。 When a topic is being displayed (eg using
lda_model.show_topics()
) you are going to get only a few words with the largest weights.当一个主题被显示时(例如使用
lda_model.show_topics()
)你只会得到几个具有最大权重的词。 This does not mean that there are no differences between topics among the remaining vocabulary.这并不意味着剩余词汇之间的主题之间没有差异。
You can steer the number of displayed words to inspect the remaining weights:您可以控制显示的单词数来检查剩余的权重:
show_topics(num_topics=4, num_words=10, log=False, formatted=True)
and change num_words
parameter to include even more words.并更改
num_words
参数以包含更多单词。
Now, there is also a possibility that:现在,还有一种可能:
minimum_probability
smaller (what is the value you use?),minimum_probability
更小(您使用的值是多少?),passes
larger,passes
次数,chunksize
smaller, chunksize
更小, I encourage you to experiment with different values of these parameters to check if any of the combination works better.我鼓励您尝试使用这些参数的不同值,以检查是否有任何组合效果更好。
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