Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance

Sigari, S; Gandomi, AH

Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance

Sigari, S Gandomi, AH

Permalink

Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Humanities and Social Sciences Communications, 2022, 9, (1), pp. 341
Issue Date:: 2022-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (1.17 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Sigari, S
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-03-20T02:35:34Z
dc.date.available	2023-03-20T02:35:34Z
dc.date.issued	2022-12-01
dc.identifier.citation	Humanities and Social Sciences Communications, 2022, 9, (1), pp. 341
dc.identifier.issn	2662-9992
dc.identifier.issn	2662-9992
dc.identifier.uri	http://hdl.handle.net/10453/167709
dc.description.abstract	The complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums, review sites, news corpora, and blogs available on the internet, product and service reviews have become an essential source of information. In a data-driven world, they will improve services and operational insights to achieve real business benefits and help enterprises remain competitive. Despite the prevalence of textual data, few studies have demonstrated the effectiveness of real-time text mining and reporting tools in firms and organizations. To address this aspect of decision-making, we have developed and evaluated an unsupervised learning system to automatically extract and classify topics and their emotion score in text streams. Data were collected from commercial websites, open-access databases, and social networks to train the model. In the experiment, the polarity score was quantified at four different levels: word, sentence, paragraph, and the entire text using Latent Dirichlet Allocation (LDA). Using subjective data mining, we demonstrate how to extract, summarize, and track various aspects of information from the Web and help traditional information retrieval (IR) systems to capture more information. An opinion tracking system presented by our model extracts subjective information, classifies them, and tracks opinions by utilizing location, time, and reviewers’ positions. Using the online-offline data collection technique, we can update the library topic in real-time to provide users with a market opinion tracker. For marketing or economic research, this approach may be useful. In the experiment, the new model is applied to a case study to demonstrate how the business process improves.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Humanities and Social Sciences Communications
dc.relation.isbasedon	10.1057/s41599-022-01325-y
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance
dc.type	Journal Article
utslib.citation.volume	9
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-20T02:35:33Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	1

Abstract:

The complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums, review sites, news corpora, and blogs available on the internet, product and service reviews have become an essential source of information. In a data-driven world, they will improve services and operational insights to achieve real business benefits and help enterprises remain competitive. Despite the prevalence of textual data, few studies have demonstrated the effectiveness of real-time text mining and reporting tools in firms and organizations. To address this aspect of decision-making, we have developed and evaluated an unsupervised learning system to automatically extract and classify topics and their emotion score in text streams. Data were collected from commercial websites, open-access databases, and social networks to train the model. In the experiment, the polarity score was quantified at four different levels: word, sentence, paragraph, and the entire text using Latent Dirichlet Allocation (LDA). Using subjective data mining, we demonstrate how to extract, summarize, and track various aspects of information from the Web and help traditional information retrieval (IR) systems to capture more information. An opinion tracking system presented by our model extracts subjective information, classifies them, and tracks opinions by utilizing location, time, and reviewers’ positions. Using the online-offline data collection technique, we can update the library topic in real-time to provide users with a market opinion tracker. For marketing or economic research, this approach may be useful. In the experiment, the new model is applied to a case study to demonstrate how the business process improves.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/167709