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πŸ“š Generate hashtags via advanced multi-label classification model



Auto-hashtag Service can actively analyze users’ messy input, learn the connection between content and hashtags, and recommend hashtags by understanding the content. What’s more, we use different models for the analysis of different media formats in one twitter and combine the results together using weights, so the system is flexible by modifying the weights when coping with various types of twitter, like sharing a funny picture or make text comments. For more details, see the full document





Getting Started


Text Analyzer

1. Crawl tweets given multiple hashtags

You should apply for Twitter API and fill the content

consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''

In the expression part, multiple hashtags are used to crawl tweets

expression = "#friends OR #pets OR #tuesdaymotivation OR #funny OR #contest OR #giveaway OR #ootd OR #win OR #merrychristmas OR #competition OR #fridayfeeling OR #traveltuesday OR #happybirthday OR #wcw OR #goals OR #fitness OR #vegan OR #movies OR #running OR #thankful OR #science OR #blessed OR #influencer OR #metoo"

2. Generate Formal Data

In this part, you should specify

textclassifier = TextClassifier(
'/content/multi-label-classification', 'dictionary.txt', 'hashtag.txt', 'input.train.text.csv')

the first parameter is working directory, the fourth is crawl data. multi-label-classification gives the example of dictionary.txt and hashtag.txt

3. Train the model and make prediction

To make a demo, I download my crawled data by

git clone

you can make your own dataset and train your own model.

Image Analyzer

All necessary parts are discussed at Document, so you just download the image model and code from Internet. This content is contributed by XiaoSanGit.


clone and repo auto-hashtag and modify the model path in


then run Django on your server

cd auto-hashtag
python3 runserver 8000


πŸ‘€ Evan - Text Analyzer

πŸ‘€ XiaoSanGit - Image Analyzer

🀝 Contributing

Contributions, issues and feature requests are welcome! Feel free to check issues page.

πŸ’° Show your support

Give a ⭐️ if this project helped you!

PayPal Patron

πŸ“– Reference

πŸ™ Acknowledgments

  • Advisor: Professor Chua Tat Seng