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Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm

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@article{IJASEIT1483,
   author = {Siti Rohaidah Ahmad and Mohd Ridzwan Yaakub and Azuraliza Abu Bakar},
   title = {Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {1120--1126},
   keywords = {Typed dependency relations; part-of-speech tags; feature; sentiment word.},
   abstract = {

Through online product reviews, consumers share their opinions, criticisms and satisfactions on the products they have purchased. However, the abundance of product reviews may be confusing and time-consuming for prospective customers as they read and analyze differing views before buying a product. The unstructured format of product reviews needs a sentiment mining approach in analyzing customers’ comments on a product and its features. In this paper, the researchers explore and analyze the hybrid role of typed dependency relations (TDR) and part-of-speech tagging (POST) in detecting the relation between features and sentiment words. The researchers have also created a list of combination rules using TDR and POST to serve as a guide in identifying the relation between features and sentiment words in sentences. Results have shown that the hybrid algorithm could assist in identifying such a relationship and improve performance.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1483},    doi = {10.18517/ijaseit.6.6.1483} }

EndNote

%A Ahmad, Siti Rohaidah
%A Yaakub, Mohd Ridzwan
%A Bakar, Azuraliza Abu
%D 2016
%T Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm
%B 2016
%9 Typed dependency relations; part-of-speech tags; feature; sentiment word.
%! Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm
%K Typed dependency relations; part-of-speech tags; feature; sentiment word.
%X 

Through online product reviews, consumers share their opinions, criticisms and satisfactions on the products they have purchased. However, the abundance of product reviews may be confusing and time-consuming for prospective customers as they read and analyze differing views before buying a product. The unstructured format of product reviews needs a sentiment mining approach in analyzing customers’ comments on a product and its features. In this paper, the researchers explore and analyze the hybrid role of typed dependency relations (TDR) and part-of-speech tagging (POST) in detecting the relation between features and sentiment words. The researchers have also created a list of combination rules using TDR and POST to serve as a guide in identifying the relation between features and sentiment words in sentences. Results have shown that the hybrid algorithm could assist in identifying such a relationship and improve performance.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1483 %R doi:10.18517/ijaseit.6.6.1483 %J International Journal on Advanced Science, Engineering and Information Technology %V 6 %N 6 %@ 2088-5334

IEEE

Siti Rohaidah Ahmad,Mohd Ridzwan Yaakub and Azuraliza Abu Bakar,"Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1120-1126, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1483.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ahmad, Siti Rohaidah
AU  - Yaakub, Mohd Ridzwan
AU  - Bakar, Azuraliza Abu
PY  - 2016
TI  - Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 1120
EP  - 1126
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Typed dependency relations; part-of-speech tags; feature; sentiment word.
N2  - 

Through online product reviews, consumers share their opinions, criticisms and satisfactions on the products they have purchased. However, the abundance of product reviews may be confusing and time-consuming for prospective customers as they read and analyze differing views before buying a product. The unstructured format of product reviews needs a sentiment mining approach in analyzing customers’ comments on a product and its features. In this paper, the researchers explore and analyze the hybrid role of typed dependency relations (TDR) and part-of-speech tagging (POST) in detecting the relation between features and sentiment words. The researchers have also created a list of combination rules using TDR and POST to serve as a guide in identifying the relation between features and sentiment words in sentences. Results have shown that the hybrid algorithm could assist in identifying such a relationship and improve performance.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1483 DO - 10.18517/ijaseit.6.6.1483

RefWorks

RT Journal Article
ID 1483
A1 Ahmad, Siti Rohaidah
A1 Yaakub, Mohd Ridzwan
A1 Bakar, Azuraliza Abu
T1 Detecting Relationship between Features and Sentiment Words using Hybrid of Typed Dependency Relations Layer and POS Tagging (TDR Layer POS Tags) Algorithm
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 1120
OP 1126
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Typed dependency relations; part-of-speech tags; feature; sentiment word.
AB 

Through online product reviews, consumers share their opinions, criticisms and satisfactions on the products they have purchased. However, the abundance of product reviews may be confusing and time-consuming for prospective customers as they read and analyze differing views before buying a product. The unstructured format of product reviews needs a sentiment mining approach in analyzing customers’ comments on a product and its features. In this paper, the researchers explore and analyze the hybrid role of typed dependency relations (TDR) and part-of-speech tagging (POST) in detecting the relation between features and sentiment words. The researchers have also created a list of combination rules using TDR and POST to serve as a guide in identifying the relation between features and sentiment words in sentences. Results have shown that the hybrid algorithm could assist in identifying such a relationship and improve performance.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1483 DO - 10.18517/ijaseit.6.6.1483