‘ Latent’ in LDA refers to finding the hidden topics and ‘ Dirichlet’ is the probability distribution used to understand how a keyword and topic are related.

Topic modelling algorithms, such as Latent Dirichlet Allocation (LDA) which we are going to use in this article, are a set of natural language processing based models used to detect underlying topics in huge corpora of text. Gensim is designed to handle large text collections.

Gensim is an amazing open-source python library used for unsupervised topic modelling and natural language processing. Ingredients to achieve topic modelling a. Hence selecting the right preprocessing methodology and having domain knowledge to understand the results of topic modelling are crucial.Ģ.

The ability to interpret a topic based on the set of keywords we get for each model.

The technique of processing the data to ensure only important information goes into the model as the model predictions are only as good as the data we train it with.

Each topic to have a set of keywords that make that particular topicĪreas of focus to achieve the best results are as follows:.

Each article to be mapped to a unique topic.

The two results we are looking at from the model in this case study are, In the same lines, here also, we first pre-process the text data and then perform topic modelling on the preprocessed data. While performing any Natural Language Processing task, we have to preprocess the text data before giving the data to the model as the predictions are as good as the data.

Dictionary: Collection of mapping of each unique word to a unique indexĪssumption: Each document is related to the corpus and no document is isolated from the corpus.

Corpus: Collection of all the documents.

Document: Each text file (article in our case study).

Topics might also be used to give the next model(which can be supervised learning) an extra analytical boost.īefore diving into the process involved, here are a few terms to know. Topics thus extracted can be used to get meaningful insights from the text data. Topic modelling allows us to understand the high dimensional text data and identify the main topics of our text data by giving the keywords of each topic. LDA, one of the most popular topic modelling algorithms Gensim, a python library to perform various NLP tasks b.

Ingredients to achieve topic modelling a.

A Brief Introduction to Topic Modelling.

The flow of the article will be as follows:

In this article, we will understand the nitty-gritty of topic modelling and perform topic modelling on Newyork Times articles from the year 2020 using a python library called, Gensim. Other interesting applications of topic modelling are indexing research papers that help researchers track research trends and identify papers to read recommendation systems to match users and news articles through clustering, sentimental analysis of reviews on a product. Tedious, right? Topic modelling comes to the rescue in such cases. Now imagine having to go through huge numbers of text documents to understand what it is about. A quick eyeball on the data would do the job. Imagine you are given text data and asked to find what the text data is about.

A Guide to Topic modelling on NYT articles to understand the trends

Get plain text topics from gensim lda