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Authors: Umesha Sandamini ; Kusal Rathnakumara ; Pasan Pramuditha ; Madushani Dissanayake ; Disni Sriyaratna ; Hansi De Silva and Dharshana Kasthurirathna

Affiliation: Faculty of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Keyword(s): Singlish, Post Recommendation, Language Identification, Transliteration, Social Media.

Abstract: Social media is an attractive means of communication which people used to exchange information. Post recommendation eliminates the overflooding of information in social media to the users’ news feed by suggesting the best matching information based on users’ preference that in return increase the usability. Social media users use different languages and their variations where most of the Sri Lankan users are accustomed to use Sinhala and Romanized Sinhala. However, post recommendation approaches used in current social media applications do not cater to code-mixed text. Therefore, this paper proposes a novel post recommendation approach that supports Singlish. The study is separated into two major components as language identification and transliteration, and post recommendation. In this study, script identification was performed using regular expressions while a Naïve Bayes classification model that accomplished 97% of accuracy was employed for language identification of Romanized te xt. Transliteration of Singlish to Sinhala was conducted using a character level seq2seq BLSTM model with a BLEU score of 0.94. Furthermore, Google translation API and YAKE were used for Sinhala-English translation and keyword extraction respectively. Post recommendation model utilized a combination of rule-based and CF techniques that accomplished the RMSE of 0.2971 and MAE of 0.2304. (More)

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Paper citation in several formats:
Sandamini, U.; Rathnakumara, K.; Pramuditha, P.; Dissanayake, M.; Sriyaratna, D.; De Silva, H. and Kasthurirathna, D. (2022). A Singlish Supported Post Recommendation Approach for Social Media. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 412-419. DOI: 10.5220/0010829700003116

@conference{icaart22,
author={Umesha Sandamini. and Kusal Rathnakumara. and Pasan Pramuditha. and Madushani Dissanayake. and Disni Sriyaratna. and Hansi {De Silva}. and Dharshana Kasthurirathna.},
title={A Singlish Supported Post Recommendation Approach for Social Media},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={412-419},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010829700003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Singlish Supported Post Recommendation Approach for Social Media
SN - 978-989-758-547-0
IS - 2184-433X
AU - Sandamini, U.
AU - Rathnakumara, K.
AU - Pramuditha, P.
AU - Dissanayake, M.
AU - Sriyaratna, D.
AU - De Silva, H.
AU - Kasthurirathna, D.
PY - 2022
SP - 412
EP - 419
DO - 10.5220/0010829700003116
PB - SciTePress

- Science and Technology Publications, Lda.
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