dc.contributor.advisor |
Thayasivam U |
|
dc.contributor.author |
Dissanayake MLS |
|
dc.date.accessioned |
2022 |
|
dc.date.available |
2022 |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Dissanayake, M.L.S. (2022). An Analytical study of pre - trained models for sentiment analysis of sinhala news comments [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22462 |
|
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/22462 |
|
dc.description.abstract |
In the area of natural language processing, due to the large-scale text data availability
sentiment analysis has become a prevalence topic. Sentiment analysis is a text
classification which is mainly focusing on classifying recommendations and reviews as
positive or negative. Earlier for this classification task, most of methods require product
reviews and label them. Using these reviews then a classifier is trained with their
relevant labels. For this training procedure a huge number of labeled data is needed to
train these classification models for each of the product, considering the facts that the
distribution of the reviews can be different between different domains and to enhance
the performance of these classification models. Nevertheless, the procedure of labeling
the data is very expensive and time consuming. For low resource languages like Sinhala
language, the existence of annotated Sinhala data is limited compared to the languages
like English language. The need of applying classification algorithms in order to perform
sentiment classification for Sinhala language is challenging. Apart from applying
traditional algorithms to analyze sentiments, here using pre-trained models(PTM)s,
experimenting on whether the outcome of these experiments outperform the traditional
methods. In natural language processing, PTM is performing an important role, since it
paves the way for applying PTMs for downstream tasks. Therefore, this research takes
the step to applying PTMs such as BERT and XLnet to classify sentiments.
Experiments have been done using two approaches on BERT model as fine tuning the
BERT model and feature based approach. Also using the existing Roberta-based Sinhala
models, named as SinBERT-small and SinBERT-large which are available in
Huggingface official site which have trained using a large Sinhala language corpus. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
TRANSFER LEARNING |
en_US |
dc.subject |
SENTIMENT ANALYSIS |
en_US |
dc.subject |
PRE-TRAINED MODELS |
en_US |
dc.subject |
SINHALA NEWS COMMENTS |
en_US |
dc.subject |
COMPUTER SCIENCE- Dissertation |
en_US |
dc.subject |
COMPUTER SCIENCE & ENGINEERING - Dissertation |
en_US |
dc.title |
An Analytical study of pre - trained models for sentiment analysis of sinhala news comments |
en_US |
dc.type |
Thesis-Abstract |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.degree |
MSc in Computer Science & Engineering |
en_US |
dc.identifier.department |
Department of Computer Science & Engineering |
en_US |
dc.date.accept |
2022 |
|
dc.identifier.accno |
TH5120 |
en_US |