This repository provides my solution for the 3rd Assignment for the course of Text Analytics for the MSc in Data Science at Athens University of Economics and Business.

Practically it is the natural development of the Sentiment Classifier with Classic methods project. The change from last time is that this project uses Multi Layer Perceptrons (MLPs) implemented in Keras

All hyper parameters are tuned on the development subset of the dataset.

More classifiers are used, in order to use them as baseline and comparison to the model created.

Precision, recall, F1 scores for each class and classifier, using a classification threshold t = 0.5 for each class, computed on the training, development, and test subsets.

Macro-averaged precision, recall, F1 scores for each classifier, again using a classification threshold t = 0.5 for each class, computed on the training, development, and test subsets.

Bootstrap statistical significance tests to check if the difference between the test macro-averaged F1 of the best (in terms of test macro-averaged F1) classifier and the test macro-averaged F1 of each other classifier (or baseline) experimented with (p-value < 0.01).

Precision-recall curves curves using macro-averaged precision and recall, computed on the test subset.Corresponding AUC.

Statistics about the datasets (e.g., average document length, number of training/dev/test documents, vocabulary size) and preprocessing on the dataset.