Abstract:
"
Sinhala is a low resource language that is spoken by 16 million people in Sri Lanka which is
the native language of Sinhalese people. Due to the lack of resources, there are only a minimal
amount of researches conducted in the territory of sentiment analysis based on the Sinhala
language when compared to other languages like English and Chinese. Most of the existing
researches have been conducted by using lexicons and dictionary-based approaches combined
with classification algorithms. With the advancements of word embedding and deep learning
techniques, recent researches have emerged with utilizing these techniques in the Sinhala
language domain for sentiment analysis and text classification tasks. Even more recent
developments in the Natural Language Processing (NLP) landscape like Bidirectional Encoder
Representations from Transformers (BERT) based language models which have achieved
state-of-the-art results for a variety of tasks in the NLP domain haven’t been applied to the
Sinhala language domain as of now.
Therefore, we introduced a sentiment analysis model for the Sinhala language by using BERT
based language model known as Language-agnostic BERT Sentence Embedding (LaBSE). The
classification is done using both binary and multiclass dataset consisting of Sinhala news
comments. An F1-score of 89.82% for the binary classification and an F1-score of 64.72%for
the multiclass classification was achieved by the newly introduced model which surpasses the
existing research achievements carried out using deep learning and static word embedding
approaches.
"
