In this repository, I'll use the gensim library to build LDA (Latent Dirichlet Allocation) to classify text in a document to a particular topic. The dataset I'll use is a list of over one million news headlines published over a period of 15 years. We'll start by loading it from the abcnews-date-text.csv file.
To learn more about what the Latent Dirichlet Allocation is and how it works, first watch the videos linked below:
you can download a copy of the project from my GitHub here and then run a Jupyter server locally with Anaconda.
- Open a terminal and clone the project repository:
$ git clone https://github.com/A2Amir/Topic-Modeling
- Switch to the project folder you cloned the project and create a conda environment (note: you must already have Anaconda installed):
$ cd to the project folder you cloned the project
$ conda env create -f nlp.yaml
- Activate the conda environment, then run the jupyter notebook server. (Note: windows users should run
activate nlp)
$ source activate nlp
$ jupyter notebook
Depending on your system settings, Jupyter will either open a browser window, or the terminal will print a URL with a security token. If the terminal prints a URL, simply copy the URL and paste it into a browser window to load the Jupyter browser. Once you load the Jupyter browser, select the project notebook Latent_dirichlet_allocation and follow the instructions inside to run it.