Integration of Opinion Mining into customer analysis model

(2015) Integration of Opinion Mining into customer analysis model. PhD thesis, Queensland University of Technology.

Description

This research proposes a multi-dimensional model for Opinion Mining, which integrates customers' characteristics and their opinions about products (or services). Customer opinions are valuable for companies to deliver right products or services to their customers. This research presents a comprehensive framework to evaluate opinions' orientation based on products' hierarchy attributes. It also provides an alternative way to obtain opinion summaries for different groups of customers and different categories of produces.

Impact and interest:

Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

604 since deposited on 24 Jul 2015
39 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 85084
Item Type: QUT Thesis (PhD)
Supervisor: Li, Yuefeng & Feng, Yanming
Keywords: Opinion Mining, Sentiment Analysis, Feature Ontology, OLAP, Data Cube, Unstructured Data
Divisions: Past > QUT Faculties & Divisions > Science & Engineering Faculty
Past > Schools > School of Chemistry, Physics & Mechanical Engineering
Past > Schools > School of Electrical Engineering & Computer Science
Institution: Queensland University of Technology
Deposited On: 24 Jul 2015 01:46
Last Modified: 05 Sep 2017 14:42