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Hash Tag Generation For Social Media Content
- 1. Hash Tag Generation For Social Media Content Group 3 IRE Project
- 2. Members ➔ Harshil Jain ➔ Syed Ahmad ➔ Krishna Chaitanya Pappu ➔ Kaleemullah Mohammed Mentor ➔ Shashank Gupta
- 3. Problem Description The objective is to develop and IR/ML system to generate hash tags for user generated Social Media containing Images as well as text.
- 4. Applications The system can be easily incorporated in various Social Media Platforms to generate hashtags. It can also be used to store metadata about the content.
- 5. Data???? An Essential Part Of all IR/ML Systems
- 6. Data Collection and Storage➔ We crawled twitter for tweets having images , text as well as hashtags using tweepy API. ➔ The image URLs were saved and the images were downloaded at a later point of time. ➔ About 15 lakh tweets were crawled, which had around 18 lakh(non-distinct) hashtags.
- 7. Data Cleaning ➔ The tweets were tokenized and a vocabulary was made out of the tweets after basic text processing. ➔ It was found that some images were removed from the web by the time we downloaded. ➔ So we had to filter these images before we moved to training the model.
- 8. Model
- 9. INput ➔ Tweet text and Image OUTput ➔ Hashtags generated on a character level
- 10. Workflow ➔ Features ◆ Image - From a pre-trained CNN ◆ Tweet - Glove embeddings fed into LSTM ➔ Then, the features are combined to give a full feature vector. ➔ Then the concatenated feature is fed to a character level Language Model(LSTM) which generates hashtags.
- 11. SYSTEM MODEL
- 13. Challenges ➔ Each entry has multiple hashtags, so feeding the data to network was an issue. ➔ We thought of a couple of approaches and ended up by feeding the hashtags as “#Hash1 #Hash2 #Hash3”. ➔ The Loss function and evaluation metric was a tough area as it is a generative model. We used Cross-Entropy as the training loss function.
- 14. REsults ➔ Results of best two epochs:- ◆ Epoch 1(100 iterns.):- ● Train perplexity: 65.9631 ● Train accuracy: 48.2185 ● Validation perplexity: 170.258 ● Validation accuracy: 41.7759 ◆ Epoch 2(200 iterns.):- ● Train perplexity: 59.322 ● Train accuracy: 49.0317 ● Validation perplexity: 175.866 ● Validation accuracy: 41.8238
- 16. THANK YOU