  Due to the high impact of the fast-evolving fields of machine learning and
deep learning, Natural Language Processing (NLP) tasks have further obtained
comprehensive performances for highly resourced languages such as English and
Chinese. However Sinhala, which is an under-resourced language with a rich
morphology, has not experienced these advancements. For sentiment analysis,
there exists only two previous research with deep learning approaches, which
focused only on document-level sentiment analysis for the binary case. They
experimented with only three types of deep learning models. In contrast, this
paper presents a much comprehensive study on the use of standard sequence
models such as RNN, LSTM, Bi-LSTM, as well as more recent state-of-the-art
models such as hierarchical attention hybrid neural networks, and capsule
networks. Classification is done at document-level but with more granularity by
considering POSITIVE, NEGATIVE, NEUTRAL, and CONFLICT classes. A data set of
15059 Sinhala news comments, annotated with these four classes and a corpus
consists of 9.48 million tokens are publicly released. This is the largest
sentiment annotated data set for Sinhala so far.
