I. Probabilistic Modelling
- TF-IDF based Vectorization w/o Lemmatization + Logistic Regression
- TF-IDF based Vectorization w/o Lemmatization + Multinomial Naive Bayes Classification
II. Deep Learning
- Selected Hyperparameter values for max_features (Number of words learned by Keras Tokenizer) & maxlen (Maximum permissible Length of Sequence).
- Unpacked GLoVe (Global Vectors for Word Representation) Embeddings for Keras API's Embedding Layer
- Trained a Recurrent Neural Network using GLoVe Embeddings and obtained an accuracy of 95.62% during Cross Validation.