https://github.com/rahul1582/topic-modelling

Identifying topics in a set of documents
https://github.com/rahul1582/topic-modelling

grouping lda nlp nltk topic-modeling

Last synced: over 1 year ago
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Identifying topics in a set of documents

• Host: GitHub
• URL: https://github.com/rahul1582/topic-modelling
• Owner: Rahul1582
• License: apache-2.0
• Created: 2019-10-10T13:15:08.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-05-15T17:57:21.000Z (about 6 years ago)
• Last Synced: 2025-02-06T09:48:26.290Z (over 1 year ago)
• Topics: grouping, lda, nlp, nltk, topic-modeling
• Language: Jupyter Notebook
• Homepage: https://nbviewer.jupyter.org/github/Rahul1582/Topic-Modelling/blob/master/Topic%20Modelling.ipynb
• Size: 4.93 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Topic-Modelling-using-Latent Dirichlet Allocation (LDA) 

Topic modeling is the process of identifying topics in a set of documents. 

This can be useful for search engines, customer service automation, and any other instance where knowing the topics of documents is important.

I used Latent Dirichlet Allocation (LDA) Algorithm.

## Dataset (optional)

The [dataset](https://www.kaggle.com/austinreese/trump-tweets) is available as a single csv file.

### Technologies Used

```

1.NLTK

2.LDA

3.Python

```

### Author 

```

Rahul Kumar Patro

```