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humor_detection's Introduction

Humor Classifier

Dataset Description

Dataset used Link

It contains five pickle files:

Positive Samples

  1. Humorous One-liners
  2. Longer Jokes

Negative Samples

  1. Reuters headlines
  2. English Proverbs
  3. Wikipedia Sentences

Conclusion:

Models Description Accuracy F1 Score
1. Simple feed-forward network with dense layers on top of embedding layer 0.9660 0.9231
2. Without pre-trained word embeddings 0.9839 0.9568
3. Using Simple RNN layer on top of embedding layer 0.9686 0.9413
4. Using LSTM layer on top of the embedding layer 0.9587 0.9514
5. Using two Conv1D layers on top of the Embedding layer 0.9674 0.9469
7. Using GRU layer on top of Conv1D layer 0.9617 0.9462
8. Using two GRU layers on top of two Conv1D layers 0.9599 0.9472

Team Members:

humor_detection's People

Contributors

iamdsc avatar marxmit7 avatar

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