Dictionary-based data augmentation for Libyan dialect

Data Augmentation (DA) is an important technique for limited resources languages to address the deficiency in data. It’s based on the generation of new instances to tackle imbalanced data. In this work, we implemented a data augmentation technique by using a dictionary to replace synonym or antonym words within the original text to generate new text. This must keep the semantic meaning of the text. In this paper, we present the Libyan Dialect Dictionary LDD which we have created in order to generate new comments/tweets from Arabic Sentiment Analysis dataset for Libyan Dialect in the Airline domain ASALDA. To evaluate the performance of sentiment classification of new augmented data, three deep learning (DL) models LSTM, CNN, and RNN were used. The result confirms that the LSTM model achieved the highest accuracy of 94% when using original data in the test set and augmented data in the train and dev set. The LDD proposed in this paper can be used for all researchers who work on Libyan dialects and data augmentation.