Demotte, PSenevirathne, LKarunanayake, BMunasinghe, URanathunga, SWeeraddana, CEdussooriya, CUSAbeysooriya, RP2022-08-092022-08-092020-07P. Demotte, L. Senevirathne, B. Karunanayake, U. Munasinghe and S. Ranathunga, "Sentiment Analysis of Sinhala News Comments using Sentence-State LSTM Networks," 2020 Moratuwa Engineering Research Conference (MERCon), 2020, pp. 283-288, doi: 10.1109/MERCon50084.2020.9185327.http://dl.lib.uom.lk/handle/123/18579Different types of deep learning based sentiment classification techniques have been introduced for sentiment analysis, and for Natural Language Processing, in general. However, Sinhala, which is a low resourced, yet morphologically rich language, has not experienced these recent advancements. In particular, for sentiment analysis, there is only one research that used deep learning techniques, and that also gave sub-optimal results. In this paper, we present the use of a novel state-of-theart deep learning technique called sentence state long short-term memory network for Sinhala sentiment classification. On a binary sentiment classification task, this method outperforms both the previously reported statistical machine learning algorithms and deep learning algorithms.enSinhalasentiment analysiss-lstmnatural language processingdeep learningConference-Full-textEngineeringEngineering Research Unit, University of Moratuwa2020Moratuwa Engineering Research Conference 2020Moratuwa, Sri Lankapp. 283-288Proceedings of Moratuwa Engineering Research Conference 2020piyumalanthony.16@cse.mrt.ac.lklahiru.16@cse.mrt.ac.lkbinod.16@cse.mrt.ac.lkudyogi.16@cse.mrt.ac.lksurangika@cse.mrt.ac.lk10.1109/MERCon50084.2020.9185327