| Problem | Data | Methods | Libs | Link |
|---|---|---|---|---|
NLP |
Text | Deep Learning - LSTM, Word2Vec |
Sklearn, Keras, Gensim, Pandas, Seaborn |
https://github.com/erdiolmezogullari/ml-deep-learning-keras-novel |
If you want to see the further ML projects, you may visit my main repo: https://github.com/erdiolmezogullari/ml-projects
This project is related to text classification problem that we tackled with Deeplearing (LSTM) model, which classifies given arbitrary paragraphes collected over 12 different novels randomly, above:
0. alice_in_wonderland
1. dracula
2. dubliners
3. great_expectations
4. hard_times
5. huckleberry_finn
6. les_miserable
7. moby_dick
8. oliver_twist
9. peter_pan
10. talw_of_two_cities
11. tom_sawyer
In other words, you can think about those novels are our target classes of our dataset.
To distinguish actual class of paragraph, the semantic latent amongst paragraphes would play an important role. Therefore, We used Deeplearing (LSTM) on top of Keras (Tensorflow) after creating an embedding matrix by Gensim's word2vec.
If there is any semantic latent amongst sentences in corresponding paragraph, We think about similar paragraphes were collected from same resources (novels) most likely.