The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To archive this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model achieves an F1 score of 84.58\% for Sinhala sentiment analysis, surpassing the current state-of-the-art model; Capsule B, which only manages to get an F1 score of 82.04\%. Further, since all the deep learning models show F1 scores above 75\% we conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.
Keywords: Natural Language Processing | Big Data | Sinhala | Machine Learning / Deep Learning | Sentiment Analysis | Social Media |
@inproceedings{weeraprameshwara2022sentiment,
title={{Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data}},
author={Weeraprameshwara, Gihan and Jayawickrama, Vihanga and de Silva, Nisansa and Wijeratne, Yudhanjaya},
abstract={The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To archive this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model achieves an F1 score of 84.58\% for Sinhala sentiment analysis, surpassing the current state-of-the-art model; Capsule B, which only manages to get an F1 score of 82.04\%. Further, since all the deep learning models show F1 scores above 75\% we conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.},
misc={https://arxiv.org/pdf/2201.03941.pdf,NLP:BIG:SIN:ML,https://goo.gl/iY6aTr},
booktitle = {2022 The 3rd International Conference on Artificial Intelligence in Electronics Engineering},
pages = {16-22},
numpages = {7},
year={2022},
publisher = {Association for Computing Machinery},
doi = {10.1145/3512826.3512829}
}
ActivePI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
ActivePI: Nisansa de Silva
We propose to build and analyze Sinhala language resources, datasets, and models to strengthen NLP research for this low-resource language, while connecting it to English and Tamil through bilingual lexicons, embeddings, and cross-lingual methods.
ActivePI: Nisansa de Silva
We propose to advance social media text analysis by combining linguistic study, preprocessing, and deep learning models to better capture sentiment, structure, and meaning in informal, large-scale user-generated text.