I am just curious to know if there is a way to automatically get the lables for the topics in Topic modelling. It would be really helpful if there's any python implementation of it.

  • $\begingroup$ Tomotopy library. It has a great python implementation too. $\endgroup$ Mar 18, 2021 at 16:32

3 Answers 3


Yes, there are ways to automatically label topics in topic modeling. One common approach is to use the top words in each topic as labels. For example, if your topic model identified the following topics:

Topic 1: ["dog", "cat", "pet", "fur", "animal"] Topic 2: ["car", "road", "speed", "engine", "wheel"] Topic 3: ["apple", "fruit", "juice", "pie", "tree"] Then you could automatically label these topics as "Pets", "Cars", and "Apples" based on the top words in each topic.

In Python, the scikit-learn library provides a convenient way to automatically label topics using this approach. Here's an example of how you could use it:

# import the necessary modules
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

# create a CountVectorizer to generate word counts from the text data
vectorizer = CountVectorizer()

# fit the vectorizer to the text data and transform it into a word count matrix
word_counts = vectorizer.fit_transform(text_data)

# create a LatentDirichletAllocation model and fit it to the word count matrix
lda = LatentDirichletAllocation()

# get the top words for each topic
top_words = [vectorizer.get_feature_names()[i] for i in lda.components_.argsort()[:, ::-1][:, :5]]

# automatically label each topic using the top words
labels = [" ".join(words) for words in top_words]

# print the labels for each topic

This code will automatically label each topic using the top 5 words in each topic, as described above. You can adjust the number of top words to use as labels by changing the value of n_top_words in the code.


Topic modelling is an unsupervised task, so by definition there is no gold-standard label. The task is a kind of clustering, i.e. it tries to group together documents with similar topics, but it doesn't label the groups.

Instead people usually use the words which are the most associated with a topic by the model as a kind of description for the topic.


Usually, the topic modelling algorithm provides a set of topics in which each topic is a collection of terms with the same semantic meaning. By default, the topics are not represented by labels. Most users choose the first word to represent that topic. I would suggest considering the first 5 words to represent that particular topic collection. This may help to get the overall insight into that topic.


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