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Twitter-Sentiment-Analysis

😈😇🗨️ Developed multiple sentiment classifiers, experimenting with the models (logistic regression, Neural nets - RNN,LSTM,GRU) and the embedding for the Twitter dataset

Twitter Sentiment Analysis

Binary classification experiments for the Twitter dataset

Notebook viewer

‼️ Because of memory restrictions, GitHub and Browsers can’t open always big jupyter notebooks. For this reason I have every notebook linked with the ✔️ jupyter nbviewer ✔️ in the following table. If you have any problems opening the notebooks, follow the links.

Notebook	Link to jupyter nbviewer	Link to Colab
BiRNN_LSTM_GRU-BestModel.ipynb
BiRNN_LSTM_GRU-Experiments.ipynb
FeedForwardNN_GloVe.ipynb
FeedForwardNN_TfiDf.ipynb
LogisticRegression.ipynb

Logistic regression

Developed a sentiment classifier using logistic regression for the Twitter sentiment classification dataset available in this link. I used the toolkit Scikit-Learn again.

Vectorization: Tf-Idf

Tf-Idf vectorization of the tweets. No pretrained vectors

Evaluation

Model metrics for evaluation: F1 score, Recall and Precision

Visualization: Confusion matrices

Feed-Forward Neural Net

Developed two sentiment classifier using feed-forward neural (pyTorch) networks for the Twitter sentiment analysis dataset.

Experimented with:

the number of hidden layers, and the number of their units
the activation functions (only the ones presented in the lectures)
the loss function
the optimizer, etc

Vectorization-1: Tf-Idf

Tf-Idf vectorization of the tweets. No pretrained vectors

Vectorization-2: Pre-trained word embendding vectors - GloVe

Vectorization made with GloVe (Stanford pre-trained embenddings)

Evaluation

Model metrics for evaluation: F1 score, Recall and Precision

Visualization: ROC curves, Loss vs Epochs, Accuracy vs Epochs and Confusion matrix

Bidirectional stacked RNN with LSTM/GRU cells

Experimented with:

the number of stacked RNNs,
the number of hidden layers,
type of cells,
skip connections,
gradient clipping and
dropout probability

Used the Adam optimizer and the binary cross-entropy loss function and transformed the predicted logits to probabilities using a sigmoid function.

Vectorization: GloVe

Pre-trained word embeddings (GloVe) as the initial embeddings to input on models.

Evaluation

Model metrics for evaluation: F1 score, Recall and Precision

Visualization: ROC curves, Loss vs Epochs, Accuracy vs Epochs and Confusion matrix

2020 - 2021