Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Mining Comparative Opinions using Multi-label Machine Learning Techniques: A case study to identify comparative opinions, based on product aspects, and their sentiment classification, in online customer reviews.
Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Abstract [en]

There is a high demand to summarize and analyze the opinions in online customer reviews. Sentiment analysis is one of the study fields in this area. Mining comparative opinions is an important application of sentiment analysis. It includes identifying the comparative opinions and the aspects that are compared. It also identifies the sentiment classification of the opinion as positive or negative. This helps businesses to make effective decisions in the development and promotion of their products and services, and to better understand their competitors. Different approaches could be used to address this sentiment analysis application, such as Machine Learning. The application is a multi-label classification problem from a machine learning perspective. This paper presents a case study to evaluate three multi-label machine learning classification techniques in addressing the problem. Empirical experiments are conducted on a domain-independent dataset of online customer reviews from Amazon for the evaluation purpose.

Place, publisher, year, edition, pages
2018. , p. 79
Keywords [en]
Sentiment Analysis, Opinion Mining, Machine Learning, Multi-label, Text Classification, Comparative, Aspect-based
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hj:diva-42974ISRN: JU-JTH-PRU-2-20190120OAI: oai:DiVA.org:hj-42974DiVA, id: diva2:1288571
Subject / course
JTH, Computer Engineering
Supervisors
Examiners
Available from: 2019-02-28 Created: 2019-02-13 Last updated: 2019-03-01Bibliographically approved

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Haj Ahmad, Yassin
By organisation
JTH, Computer Science and Informatics
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 101 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf