ICEF-NLP/bcms-bertic-senticomments-sr-fourway

BERTić-SentiComments-SR-Four-way

BERTić-SentiComments-SR-Four-way is a variant of the BERTić model, fine-tuned on the task of four-way sentiment classification of Serbian short texts. It differentiates between objective (NS), ambiguous/mixed (M), positive (+1), and negative texts (-1). The model was fine-tuned for 5 epochs on the SentiComments.SR dataset.

Benchmarking

This model was evaluated on the task of four-way sentiment classification of short texts in Serbian from the SentiComments.SR dataset and compared to multilingual BERT. Different lengths of fine-tuning were considered, ranging from 1 to 5 epochs. Linear classifiers relying on bag-of-words (BOW) and/or bag-of-embeddings (BOE) features were used as baselines.

Since the dataset is imbalanced, weighted F1 measure was utilized as the performance metric. Model fine-tuning and evaluation were performed using 10-fold stratified cross-validation. The code and data to run these experiments are available on the SentiComments.SR GitHub repository.

Results

References

If you wish to use this model in your paper or project, please cite the following papers:

• —[A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts](https://doi.org/10.1371/journal.pone.0242050), Vuk Batanović, Miloš Cvetanović, Boško Nikolić, PLoS ONE, 15(11): e0242050 (2020).
• —[Semantic Similarity and Sentiment Analysis of Short Texts in Serbian](https://doi.org/10.1109/TELFOR52709.2021.9653390), Vuk Batanović, in Proceedings of the 29th Telecommunications Forum (TELFOR 2021), Belgrade, Serbia (2021).