{ "source_file": "0930", "source_platform": "Medium", "author": "Shashank Kapadia", "title_listed": "Topic Modeling in Python: Latent Dirichlet Allocation (LDA)", "keywords": "", "domain": "文本处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/end-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0/", "publish_date": "2019-04-14", "corpus_stage": "", "notes": "", "id": "4588498562a8", "collected_at": "2026-10-02T10:04:55", "title": "Topic Modeling in Python: Latent Dirichlet Allocation (LDA) | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Topic Modeling in Python: Latent Dirichlet Allocation (LDA)\nHow to get started with topic modeling using LDA in Python\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nApril 14, 20196 min read\n![](../images/000.jpeg)\n···\n**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\n···\n## Introduction\nTopic 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:\n**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}\n**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\n**Tagging** , abstract “topics” that occur in a collection of documents that best represents the information in them.\nThere 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)_\nIn this article, we’ll take a closer look at LDA, and implement our first topic model using the _sklearn_ implementation in _python 2.7_\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## Theoretical Overview\nLDA 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.\n![](../images/001.png)\n\nWe 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:\n_psi_ , the distribution of words for each topic _K_\n_phi_ , the distribution of topics for each document _i_\n## **Parameters of LDA**\n> **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._\n> **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._\n···\n## LDA Implementation\nThe 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)\n 1. Loading data\n 2. Data cleaning\n 3. Exploratory analysis\n 4. Preparing data for LDA analysis\n 5. LDA model training\n 6. Analyzing LDA model results\n\n\n## Loading data\nFor 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.\nLet’s start by looking at the content of the file\npython\n\n```\n# Importing modules\nimport pandas as pd\nimport os\n\nos.chdir('..')\n\n# Read data into papers\npapers = pd.read_csv('./data/NIPS Papers/papers.csv')\n\n# Print head\npapers.head()\n```\n\n![](../images/body-1fea7def083d255ca5bf.png)\nSample of raw data\n## Data Cleaning\nSince 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\npython\n\n```\n# Remove the columns\npapers = papers.drop(columns=['id', 'event_type', 'pdf_name'], \naxis=1).sample(100)\n\n# Print out the first rows of papers\npapers.head()\n```\n\n![](../images/body-120dbd2ec7a4815f3828.png)\n**Remove punctuation/lower casing**\nNext, 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\npython\n\n```\n# Load the regular expression library\nimport re\n\n# Remove punctuation\npapers['paper_text_processed'] = \\\npapers['paper_text'].map(lambda x: re.sub('[,\\.!?]', '', x))\n\n# Convert the titles to lowercase\npapers['paper_text_processed'] = \\\npapers['paper_text_processed'].map(lambda x: x.lower())\n\n# Print out the first rows of papers\npapers['paper_text_processed'].head()\n```\n\n![](../images/body-610ba47811b2d2e59447.png)\n## **Exploratory Analysis**\nTo 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.\npython\n\n```\n# Import the wordcloud library\nfrom wordcloud import WordCloud\n\n# Join the different processed titles together.\nlong_string = ','.join(list(papers['paper_text_processed'].values))\n\n# Create a WordCloud object\nwordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')\n\n# Generate a word cloud\nwordcloud.generate(long_string)\n\n# Visualize the word cloud\nwordcloud.to_image()\n```\n\n![](../images/body-85bc25defcedd9fdc10d.png)\n## **Prepare data for LDA Analysis**\nNext, 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.\npython\n\n```\nimport gensim\nfrom gensim.utils import simple_preprocess\nimport nltk\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\n\nstop_words = stopwords.words('english')\nstop_words.extend(['from', 'subject', 're', 'edu', 'use'])\n\ndef sent_to_words(sentences):\n for sentence in sentences:\n # deacc=True removes punctuations\n yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))\n\ndef remove_stopwords(texts):\n return [[word for word in simple_preprocess(str(doc)) \n if word not in stop_words] for doc in texts]\n\ndata = papers.paper_text_processed.values.tolist()\ndata_words = list(sent_to_words(data))\n\n# remove stop words\ndata_words = remove_stopwords(data_words)\n\nprint(data_words[:1][0][:30])\n```\n\n![](../images/body-97dec3ffd0ca848bf957.png)\npython\n\n```\nimport gensim.corpora as corpora\n\n# Create Dictionary\nid2word = corpora.Dictionary(data_words)\n\n# Create Corpus\ntexts = data_words\n\n# Term Document Frequency\ncorpus = [id2word.doc2bow(text) for text in texts]\n\n# View\nprint(corpus[:1][0][:30])\n```\n\n![](../images/body-425452de721a9e771d24.png)\n## **LDA model training**\nTo 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.\npython\n\n```\nfrom pprint import pprint\n\n# number of topics\nnum_topics = 10\n\n# Build LDA model\nlda_model = gensim.models.LdaMulticore(corpus=corpus,\n id2word=id2word,\n num_topics=num_topics)# Print the Keyword in the 10 topics\npprint(lda_model.print_topics())\ndoc_lda = lda_model[corpus]\n```\n\n![](../images/body-7db1d8e1b1de5e4d28d4.png)\n## **Analyzing LDA model results**\nNow 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:\n 1. Better understanding and interpreting individual topics, and\n 2. Better understanding the relationships between the topics.\n\n\nFor (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.\nFor (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.\npython\n\n```\nimport pyLDAvis.gensim\nimport pickle \nimport pyLDAvis\n\n# Visualize the topics\npyLDAvis.enable_notebook()\n\nLDAvis_data_filepath = \nos.path.join('./results/ldavis_prepared_'+str(num_topics))\n\n# # this is a bit time consuming - make the if statement True\n# # if you want to execute visualization prep yourself\nif 1 == 1:\n LDAvis_prepared = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n with open(LDAvis_data_filepath, 'wb') as f:\n pickle.dump(LDAvis_prepared, f)\n\n# load the pre-prepared pyLDAvis data from disk\nwith open(LDAvis_data_filepath, 'rb') as f:\n LDAvis_prepared = pickle.load(f)\n\npyLDAvis.save_html(LDAvis_prepared, './results/ldavis_prepared_'+ str(num_topics) +'.html')\n\nLDAvis_prepared\n```\n\n![](../images/body-4daa0c861ee0ea707ebb.png)\n## Closing Notes\nMachine 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.\nOften, 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.\nIn 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.\n**References:**\n[1] Topic model — Wikipedia. \n[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)\n[3] Topic Mapping — Software — Resources — Amaral Lab. \n[4] A Survey of Topic Modeling in Text Mining. \nThanks 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)_.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[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/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Topic Modeling in Python: Latent Dirichlet Allocation (LDA)\nHow to get started with topic modeling using LDA in Python\n[Shashank Kapadia](https://towardsdatascience.com/author/shashank%20kapadia/)\nApril 14, 20196 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-15.jpeg)\n···\n**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\n···\n## Introduction\nTopic 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:\n**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}\n**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\n**Tagging** , abstract “topics” that occur in a collection of documents that best represents the information in them.\nThere 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)_\nIn this article, we’ll take a closer look at LDA, and implement our first topic model using the _sklearn_ implementation in _python 2.7_\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n## Theoretical Overview\nLDA 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.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-128-1024x603.png)\n\nWe 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:\n_psi_ , the distribution of words for each topic _K_\n_phi_ , the distribution of topics for each document _i_\n## **Parameters of LDA**\n> **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._\n> **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._\n···\n## LDA Implementation\nThe 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)\n 1. Loading data\n 2. Data cleaning\n 3. Exploratory analysis\n 4. Preparing data for LDA analysis\n 5. LDA model training\n 6. Analyzing LDA model results\n\n\n## Loading data\nFor 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.\nLet’s start by looking at the content of the file\npython\n\n```\n# Importing modules\nimport pandas as pd\nimport os\n\nos.chdir('..')\n\n# Read data into papers\npapers = pd.read_csv('./data/NIPS Papers/papers.csv')\n\n# Print head\npapers.head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-129-1024x229.png)\nSample of raw data\n## Data Cleaning\nSince 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\npython\n\n```\n# Remove the columns\npapers = papers.drop(columns=['id', 'event_type', 'pdf_name'], \naxis=1).sample(100)\n\n# Print out the first rows of papers\npapers.head()\n```\n\n![](https://assets.insightmediagroup.io/media/v2/resize:fit:1400/1*fo4WgizEBAXc02ADEXsd1A.png)\n**Remove punctuation/lower casing**\nNext, 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\npython\n\n```\n# Load the regular expression library\nimport re\n\n# Remove punctuation\npapers['paper_text_processed'] = \\\npapers['paper_text'].map(lambda x: re.sub('[,\\.!?]', '', x))\n\n# Convert the titles to lowercase\npapers['paper_text_processed'] = \\\npapers['paper_text_processed'].map(lambda x: x.lower())\n\n# Print out the first rows of papers\npapers['paper_text_processed'].head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-130-1024x137.png)\n## **Exploratory Analysis**\nTo 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.\npython\n\n```\n# Import the wordcloud library\nfrom wordcloud import WordCloud\n\n# Join the different processed titles together.\nlong_string = ','.join(list(papers['paper_text_processed'].values))\n\n# Create a WordCloud object\nwordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')\n\n# Generate a word cloud\nwordcloud.generate(long_string)\n\n# Visualize the word cloud\nwordcloud.to_image()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-131.png)\n## **Prepare data for LDA Analysis**\nNext, 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.\npython\n\n```\nimport gensim\nfrom gensim.utils import simple_preprocess\nimport nltk\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\n\nstop_words = stopwords.words('english')\nstop_words.extend(['from', 'subject', 're', 'edu', 'use'])\n\ndef sent_to_words(sentences):\n for sentence in sentences:\n # deacc=True removes punctuations\n yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))\n\ndef remove_stopwords(texts):\n return [[word for word in simple_preprocess(str(doc)) \n if word not in stop_words] for doc in texts]\n\ndata = papers.paper_text_processed.values.tolist()\ndata_words = list(sent_to_words(data))\n\n# remove stop words\ndata_words = remove_stopwords(data_words)\n\nprint(data_words[:1][0][:30])\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-132-1024x188.png)\npython\n\n```\nimport gensim.corpora as corpora\n\n# Create Dictionary\nid2word = corpora.Dictionary(data_words)\n\n# Create Corpus\ntexts = data_words\n\n# Term Document Frequency\ncorpus = [id2word.doc2bow(text) for text in texts]\n\n# View\nprint(corpus[:1][0][:30])\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-133-1024x91.png)\n## **LDA model training**\nTo 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.\npython\n\n```\nfrom pprint import pprint\n\n# number of topics\nnum_topics = 10\n\n# Build LDA model\nlda_model = gensim.models.LdaMulticore(corpus=corpus,\n id2word=id2word,\n num_topics=num_topics)# Print the Keyword in the 10 topics\npprint(lda_model.print_topics())\ndoc_lda = lda_model[corpus]\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-134-1024x894.png)\n## **Analyzing LDA model results**\nNow 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:\n 1. Better understanding and interpreting individual topics, and\n 2. Better understanding the relationships between the topics.\n\n\nFor (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.\nFor (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.\npython\n\n```\nimport pyLDAvis.gensim\nimport pickle \nimport pyLDAvis\n\n# Visualize the topics\npyLDAvis.enable_notebook()\n\nLDAvis_data_filepath = \nos.path.join('./results/ldavis_prepared_'+str(num_topics))\n\n# # this is a bit time consuming - make the if statement True\n# # if you want to execute visualization prep yourself\nif 1 == 1:\n LDAvis_prepared = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n with open(LDAvis_data_filepath, 'wb') as f:\n pickle.dump(LDAvis_prepared, f)\n\n# load the pre-prepared pyLDAvis data from disk\nwith open(LDAvis_data_filepath, 'rb') as f:\n LDAvis_prepared = pickle.load(f)\n\npyLDAvis.save_html(LDAvis_prepared, './results/ldavis_prepared_'+ str(num_topics) +'.html')\n\nLDAvis_prepared\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/03/image-135-1024x640.png)\n## Closing Notes\nMachine 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.\nOften, 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.\nIn 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.\n**References:**\n[1] Topic model — Wikipedia. \n[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)\n[3] Topic Mapping — Software — Resources — Amaral Lab. \n[4] A Survey of Topic Modeling in Text Mining. \nThanks 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)_.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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