The Facebook network allows its users to record their reactions to text via a typology of emotions. This network, taken at scale, is therefore a prime data set of annotated sentiment data. This paper uses millions of such reactions, derived from a decade worth of Facebook post data centred around a Sri Lankan context, to model an eye of the beholder approach to sentiment detection for online Sinhala textual content. Three different sentiment analysis models are built, taking into account a limited subset of reactions, all reactions, and another that derives a positive/negative star rating value. The efficacy of these models in capturing the reactions of the observers are then computed and discussed. The analysis reveals that binary classification of reactions, for Sinhala content, is significantly more accurate than the other approaches. Furthermore, the inclusion of the like reaction hinders the capability of accurately predicting other reactions.
Keywords: Natural Language Processing | Big Data | Sinhala | Machine Learning / Deep Learning | Sentiment Analysis | Social Media | Word Vectorization |
@inproceedings{jayawickrama2021seeking,
title={Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts},
author={Jayawickrama, Vihanga and Weeraprameshwara, Gihan and de Silva, Nisansa and Wijeratne, Yudhanjaya},
booktitle={2021 21st International Conference on Advances in ICT for Emerging Regions (ICter)},
year={2021},
abstract={The Facebook network allows its users to record their reactions to text via a typology of emotions. This network, taken at scale, is therefore a prime data set of annotated sentiment data. This paper uses millions of such reactions, derived from a decade worth of Facebook post data centred around a Sri Lankan context, to model an eye of the beholder approach to sentiment detection for online Sinhala textual content. Three different sentiment analysis models are built, taking into account a limited subset of reactions, all reactions, and another that derives a positive/negative star rating value. The efficacy of these models in capturing the reactions of the observers are then computed and discussed. The analysis reveals that binary classification of reactions, for Sinhala content, is significantly more accurate than the other approaches. Furthermore, the inclusion of the like reaction hinders the capability of accurately predicting other reactions.},
pages={177-182},
doi={10.1109/ICter53630.2021.9774796},
misc={https://arxiv.org/abs/2112.00468,NLP:BIG:SIN:ML,https://pbs.twimg.com/media/FF-ajXuWUAEuDS_?format=jpg&name=4096x4096}
}
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.