Start Submission Become a Reviewer

Reading: Facebook for Sentiment Analysis: Baseline Models to Predict Facebook Reactions of Sinhala Posts

Download

A- A+
Alt. Display

Articles

Facebook for Sentiment Analysis: Baseline Models to Predict Facebook Reactions of Sinhala Posts

Authors:

Vihanga Jayawickrama ,

University of Moratuwa, LK
About Vihanga
Department of Computer Science & Engineering
X close

Gihan Weeraprameshwara,

University of Moratuwa, LK
About Gihan
Department of Computer Science & Engineering
X close

Nisansa de Silva,

University of Moratuwa, LK
About Nisansa
Department of Computer Science & Engineering
X close

Yudhanjaya Wijeratne

LIRNEasia, LK
X close

Abstract

Research on natural language processing in most regional languages is hindered due to resource poverty. A possible solution for this is utilization of social media data in research. For example, 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 dataset 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 the Star Rating Model, for Sinhala content, is significantly more accurate (0.82) than the other approaches. The inclusion of the like reaction is discovered to hinder the capability of accurately predicting other reactions. Furthermore, this study provides evidence for the applicability of social media data to eradicate the resource poverty surrounding languages such as Sinhala.
How to Cite: Jayawickrama, V., Weeraprameshwara, G., de Silva, N. and Wijeratne, Y., 2022. Facebook for Sentiment Analysis: Baseline Models to Predict Facebook Reactions of Sinhala Posts. International Journal on Advances in ICT for Emerging Regions (ICTer), 15(2), pp.22–32. DOI: http://doi.org/10.4038/icter.v15i2.7248
Published on 08 Nov 2022.
Peer Reviewed

Downloads

  • PDF (EN)

    comments powered by Disqus