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Sign in[Submit an Article](https://write.insightmediagroup.io) [Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Data Science](https://towardsdatascience.com/category/data-science/) # Topic Modeling in Python: Latent Dirichlet Allocation (LDA) How to get started with topic modeling using LDA in Python [Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/) April 14, 20196 min read ![](../images/000.jpeg) ··· **Preface:** This article aims to provide consolidated information on the underlying topic and is not to be considered as the original work. The information and the code are repurposed through several online articles, research papers, books, and open-source code ··· ## Introduction Topic Models, in a nutshell, are a type of statistical language models used for uncovering hidden structure in a collection of texts. In a practical and more intuitively, you can think of it as a task of: **Dimensionality Reduction** , where rather than representing a text _T_ in its feature space as {Word_i: count(Word_i, T) for Word_i in Vocabulary}, you can represent it in a topic space as {Topic_i: Weight(Topic_i, T) for Topic_i in Topics} **Unsupervised Learning** , where it can be compared to clustering, as in the case of clustering, the number of topics, like the number of clusters, is an output parameter. By doing topic modeling, we build clusters of words rather than clusters of texts. A text is thus a mixture of all the topics, each having a specific weight **Tagging** , abstract “topics” that occur in a collection of documents that best represents the information in them. There are several existing algorithms you can use to perform the topic modeling. The most common of it are, _Latent Semantic Analysis (LSA/LSI), Probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet Allocation (LDA)_ In this article, we’ll take a closer look at LDA, and implement our first topic model using the _sklearn_ implementation in _python 2.7_ Learn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap. ## Theoretical Overview LDA is a generative probabilistic model that assumes each topic is a mixture over an underlying set of words, and each document is a mixture of over a set of topic probabilities. ![](../images/001.png) We can describe the generative process of LDA as, given the _M_ number of documents, _N_ number of words, and prior _K_ number of topics, the model trains to output: _psi_ , the distribution of words for each topic _K_ _phi_ , the distribution of topics for each document _i_ ## **Parameters of LDA** > **Alpha parameter** is Dirichlet prior concentration parameter that represents document-topic density — with _a higher alpha, documents are assumed to be made up of more topics and result in more specific topic distribution per document._ > **Beta parameter** is the same prior concentration parameter that represents topic-word density — _with high beta, topics are assumed to made of up most of the words and result in a more specific word distribution per topic._ ··· ## LDA Implementation The complete code is available as a [Jupyter Notebook on GitHub](https://github.com/kapadias/medium-articles/blob/master/natural-language-processing/topic-modeling/Introduction%20to%20Topic%20Modeling.ipynb) 1. Loading data 2. Data cleaning 3. Exploratory analysis 4. Preparing data for LDA analysis 5. LDA model training 6. Analyzing LDA model results ## Loading data For this tutorial, we’ll use the dataset of papers published in NeurIPS (NIPS) conference which is one of the most prestigious yearly events in the machine learning community. The CSV data file contains information on the different NeurIPS papers that were published from 1987 until 2016 (29 years!). These papers discuss a wide variety of topics in machine learning, from neural networks to optimization methods, and many more. Let’s start by looking at the content of the file python ``` # Importing modules import pandas as pd import os os.chdir('..') # Read data into papers papers = pd.read_csv('./data/NIPS Papers/papers.csv') # Print head papers.head() ``` ![](../images/body-1fea7def083d255ca5bf.png) Sample of raw data ## Data Cleaning Since the goal of this analysis is to perform topic modeling, let’s focus only on the text data from each paper, and drop other metadata columns. Also, for the demonstration, we’ll only look at 100 papers python ``` # Remove the columns papers = papers.drop(columns=['id', 'event_type', 'pdf_name'], axis=1).sample(100) # Print out the first rows of papers papers.head() ``` ![](../images/body-120dbd2ec7a4815f3828.png) **Remove punctuation/lower casing** Next, let’s perform a simple preprocessing on the content of _paper_text_ column to make them more amenable for analysis, and reliable results. To do that, we’ll use a regular expression to remove any punctuation, and then _lowercase_ the text python ``` # Load the regular expression library import re # Remove punctuation papers['paper_text_processed'] = \ papers['paper_text'].map(lambda x: re.sub('[,\.!?]', '', x)) # Convert the titles to lowercase papers['paper_text_processed'] = \ papers['paper_text_processed'].map(lambda x: x.lower()) # Print out the first rows of papers papers['paper_text_processed'].head() ``` ![](../images/body-610ba47811b2d2e59447.png) ## **Exploratory Analysis** To verify whether the preprocessing, we’ll make a word cloud using the [wordcloud](https://github.com/amueller/word_cloud) package to get a visual representation of most common words. It is key to understanding the data and ensuring we are on the right track, and if any more preprocessing is necessary before training the model. python ``` # Import the wordcloud library from wordcloud import WordCloud # Join the different processed titles together. long_string = ','.join(list(papers['paper_text_processed'].values)) # Create a WordCloud object wordcloud = WordCloud(background_color="white", max_words=5000, contour_width=3, contour_color='steelblue') # Generate a word cloud wordcloud.generate(long_string) # Visualize the word cloud wordcloud.to_image() ``` ![](../images/body-85bc25defcedd9fdc10d.png) ## **Prepare data for LDA Analysis** Next, let’s work to transform the textual data in a format that will serve as an input for training LDA model. We start by tokenizing the text and removing stopwords. Next, we convert the tokenized object into a corpus and dictionary. python ``` import gensim from gensim.utils import simple_preprocess import nltk nltk.download('stopwords') from nltk.corpus import stopwords stop_words = stopwords.words('english') stop_words.extend(['from', 'subject', 're', 'edu', 'use']) def sent_to_words(sentences): for sentence in sentences: # deacc=True removes punctuations yield(gensim.utils.simple_preprocess(str(sentence), deacc=True)) def remove_stopwords(texts): return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts] data = papers.paper_text_processed.values.tolist() data_words = list(sent_to_words(data)) # remove stop words data_words = remove_stopwords(data_words) print(data_words[:1][0][:30]) ``` ![](../images/body-97dec3ffd0ca848bf957.png) python ``` import gensim.corpora as corpora # Create Dictionary id2word = corpora.Dictionary(data_words) # Create Corpus texts = data_words # Term Document Frequency corpus = [id2word.doc2bow(text) for text in texts] # View print(corpus[:1][0][:30]) ``` ![](../images/body-425452de721a9e771d24.png) ## **LDA model training** To keep things simple, we’ll keep all the parameters to default except for inputting the number of topics. For this tutorial, we will build a model with 10 topics where each topic is a combination of keywords, and each keyword contributes a certain weightage to the topic. python ``` from pprint import pprint # number of topics num_topics = 10 # Build LDA model lda_model = gensim.models.LdaMulticore(corpus=corpus, id2word=id2word, num_topics=num_topics)# Print the Keyword in the 10 topics pprint(lda_model.print_topics()) doc_lda = lda_model[corpus] ``` ![](../images/body-7db1d8e1b1de5e4d28d4.png) ## **Analyzing LDA model results** Now that we have a trained model let’s visualize the topics for interpretability. To do so, we’ll use a popular visualization package, [pyLDAvis](https://github.com/bmabey/pyLDAvis) which is designed to help interactively with: 1. Better understanding and interpreting individual topics, and 2. Better understanding the relationships between the topics. For (1), you can manually select each topic to view its top most frequent and/or “relevant” terms, using different values of the λ parameter. This can help when you’re trying to assign a human interpretable name or “meaning” to each topic. For (2), exploring the _Intertopic Distance Plot_ can help you learn about how topics relate to each other, including potential higher-level structure between groups of topics. python ``` import pyLDAvis.gensim import pickle import pyLDAvis # Visualize the topics pyLDAvis.enable_notebook() LDAvis_data_filepath = os.path.join('./results/ldavis_prepared_'+str(num_topics)) # # this is a bit time consuming - make the if statement True # # if you want to execute visualization prep yourself if 1 == 1: LDAvis_prepared = pyLDAvis.gensim.prepare(lda_model, corpus, id2word) with open(LDAvis_data_filepath, 'wb') as f: pickle.dump(LDAvis_prepared, f) # load the pre-prepared pyLDAvis data from disk with open(LDAvis_data_filepath, 'rb') as f: LDAvis_prepared = pickle.load(f) pyLDAvis.save_html(LDAvis_prepared, './results/ldavis_prepared_'+ str(num_topics) +'.html') LDAvis_prepared ``` ![](../images/body-4daa0c861ee0ea707ebb.png) ## Closing Notes Machine learning has become increasingly popular over the past decade, and recent advances in computational availability have led to exponential growth to people looking for ways how new methods can be incorporated to advance the field of Natural Language Processing. Often, we treat topic models as black-box algorithms, but hopefully, this article addressed to shed light on the underlying math, and intuitions behind it, and high-level code to get you started with any textual data. In the next article, we’ll go one step deeper into understanding how you can evaluate the performance of topic models, tune its hyper-parameters to get more intuitive and reliable results. **References:** [1] Topic model — Wikipedia. [2] Distributed Strategies for Topic Modeling. [https://www.ideals.illinois.edu/bitstream/handle/2142/46405/ParallelTopicModels.pdf?sequence=2&isAllowed=y](https://www.ideals.illinois.edu/bitstream/handle/2142/46405/ParallelTopicModels.pdf?sequence=2&isAllowed=y) [3] Topic Mapping — Software — Resources — Amaral Lab. [4] A Survey of Topic Modeling in Text Mining. Thanks for reading. _If you have any feedback, please feel to reach out by commenting on this post, messaging me on_[ _LinkedIn_](https://www.linkedin.com/in/shashankkapadia/) _, or shooting me an email (shmkapadia[at]gmail.com)_. ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * SK Written By ## [Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/) [See all from Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/) [Data Science](https://towardsdatascience.com/tag/data-science/)[In Depth Analysis](https://towardsdatascience.com/tag/in-depth-analysis/)[Latent Dirichlet Allocation](https://towardsdatascience.com/tag/latent-dirichlet-allocation/)[Python](https://towardsdatascience.com/tag/python/)[Topic Modeling](https://towardsdatascience.com/tag/topic-modeling/) Share This Article * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fend-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0%2F&title=Topic%20Modeling%20in%20Python%3A%20Latent%20Dirichlet%20Allocation%20\(LDA\)) * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fend-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0%2F&title=Topic%20Modeling%20in%20Python%3A%20Latent%20Dirichlet%20Allocation%20\(LDA\)) * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fend-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0%2F&text=Topic%20Modeling%20in%20Python%3A%20Latent%20Dirichlet%20Allocation%20\(LDA\)) Towards Data Science is a community publication. 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