Detecting relationship between features and sentiment words using hybrid of typed dependency relations layer and POS tagging (TDR Layer POS Tags) algorithm

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1120-1126
Number of pages7
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume6
Issue number6
DOIs
Publication statusPublished - 2016

Fingerprint

Research Personnel
researchers
purchasing
Dependency (Psychology)

Keywords

  • Feature
  • Part-of-speech tags
  • Sentiment word
  • Typed dependency relations

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Computer Science(all)
  • Engineering(all)

Cite this

@article{d5fb76a9e9c4444f9c14d69f629a136e,
title = "Detecting relationship between features and sentiment words using hybrid of typed dependency relations layer and POS tagging (TDR Layer POS Tags) algorithm",
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.",
keywords = "Feature, Part-of-speech tags, Sentiment word, Typed dependency relations",
author = "Ahmad, {Siti Rohaidah} and Yaakub, {Mohd Ridzwan} and {Abu Bakar}, Azuraliza",
year = "2016",
doi = "10.18517/ijaseit.6.6.1483",
language = "English",
volume = "6",
pages = "1120--1126",
journal = "International Journal on Advanced Science, Engineering and Information Technology",
issn = "2088-5334",
publisher = "INSIGHT - Indonesian Society for Knowledge and Human Development",
number = "6",

}

TY - JOUR

T1 - Detecting relationship between features and sentiment words using hybrid of typed dependency relations layer and POS tagging (TDR Layer POS Tags) algorithm

AU - Ahmad, Siti Rohaidah

AU - Yaakub, Mohd Ridzwan

AU - Abu Bakar, Azuraliza

PY - 2016

Y1 - 2016

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.

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.

KW - Feature

KW - Part-of-speech tags

KW - Sentiment word

KW - Typed dependency relations

UR - http://www.scopus.com/inward/record.url?scp=85010217250&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85010217250&partnerID=8YFLogxK

U2 - 10.18517/ijaseit.6.6.1483

DO - 10.18517/ijaseit.6.6.1483

M3 - Article

VL - 6

SP - 1120

EP - 1126

JO - International Journal on Advanced Science, Engineering and Information Technology

JF - International Journal on Advanced Science, Engineering and Information Technology

SN - 2088-5334

IS - 6

ER -