dc.contributor.author |
De Mel, NM |
|
dc.contributor.author |
Hettiarachchi, HH |
|
dc.contributor.author |
Madusanka, WPD |
|
dc.contributor.author |
Malaka, GL |
|
dc.contributor.author |
Perera, AS |
|
dc.contributor.author |
Kohomban, U |
|
dc.contributor.editor |
Jayasekara, AGBP |
|
dc.contributor.editor |
Bandara, HMND |
|
dc.contributor.editor |
Amarasinghe, YWR |
|
dc.date.accessioned |
2022-09-09T03:05:51Z |
|
dc.date.available |
2022-09-09T03:05:51Z |
|
dc.date.issued |
2016-04 |
|
dc.identifier.citation |
N. M. De Mel, H. H. Hettiarachchi, W. P. D. Madusanka, G. L. Malaka, A. S. Perera and U. Kohomban, "Machine learning approach to recognize subject based sentiment values of reviews," 2016 Moratuwa Engineering Research Conference (MERCon), 2016, pp. 6-11, doi: 10.1109/MERCon.2016.7480107. |
en_US |
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/18996 |
|
dc.description.abstract |
Due to the increase in the number of people
participating online on reviewing travel related entities such as
hotels, cities and attractions, there is a rich corpus of textual
information available online. However, to make a decision on a
certain entity, one has to read many such reviews manually, which
is inconvenient. To make sense of the reviews, the essential first
step is to understand the semantics that lie therein. This paper
discusses a system that uses machine learning based classifiers to
label the entities found in text into semantic concepts defined in
an ontology. A subject classifier with a precision of 0.785 and a
sentiment classifier with a correlation coefficient of 0.9423 was
developed providing sufficient accuracy for subject categorization
and sentiment evaluation in the proposed system. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/7480107/ |
en_US |
dc.subject |
Text classification |
en_US |
dc.subject |
sentiment analysis |
en_US |
dc.subject |
machine learning |
en_US |
dc.subject |
feature engineering |
en_US |
dc.title |
Machine learning approach to recognize subject based sentiment values of reviews |
en_US |
dc.type |
Conference-Full-text |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.department |
Engineering Research Unit, University of Moratuwa |
en_US |
dc.identifier.year |
2016 |
en_US |
dc.identifier.conference |
2016 Moratuwa Engineering Research Conference (MERCon) |
en_US |
dc.identifier.place |
Moratuwa, Sri Lanka |
en_US |
dc.identifier.pgnos |
pp. 6-11 |
en_US |
dc.identifier.proceeding |
Proceedings of 2016 Moratuwa Engineering Research Conference (MERCon) |
en_US |
dc.identifier.email |
madhawi.11@cse.mrt.ac.lk |
en_US |
dc.identifier.email |
hansi.11@cse.mrt.ac.lk |
en_US |
dc.identifier.email |
danushka.11@cse.mrt.ac.lk |
en_US |
dc.identifier.email |
glmalaka.11@cse.mrt.ac.lk |
en_US |
dc.identifier.email |
shehang@cse.mrt.ac.lk |
en_US |
dc.identifier.email |
upali@codegen.co.uk |
en_US |
dc.identifier.doi |
10.1109/MERCon.2016.7480107 |
en_US |