research-article

Opinion analysis for business intelligence applications

Authors:

Horacio Saggion,

Kalina Bontcheva,

Christian LeiboldAuthors Info & Claims

OBI '08: Proceedings of the first international workshop on Ontology-supported business intelligence

Article No.: 3, Pages 1 - 9

https://doi.org/10.1145/1452567.1452570

Published: 27 October 2008 Publication History

Abstract

More than ever before, business analysts have access to public forums in which opinions and sentiments about companies, products, and policies are expressed in unstructured form. Mining information from public sources is of great importance to many business intelligence applications such as credit rating or company reputation.

We have implemented a supervised machine-learning system which uses linguistic information to classify text by rating (good or bad, for example, or 1 to 5 stars). In an evaluation we have obtained good results in comparison with the state-of-the-art in opinion mining.

We are further developing the system to classify each text according to a "qualitative variable" category from an ontology specially developed for Business Intelligence (BI). This work will allow us to generate RDF statements to populate a knowledge base for BI.

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Cited By

Chatterjee DMukherjee AMukhopadhyay SPanday MPanigrahi PGoswami S(2021)A Survey on Sentiment AnalysisEmerging Technologies in Data Mining and Information Security10.1007/978-981-33-4367-2_26(259-271)Online publication date: 5-May-2021
https://doi.org/10.1007/978-981-33-4367-2_26
Gujjar J PH R P(2020)Opinion Mining for the Customer Feedback using TextBlobInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT206418(72-76)Online publication date: 15-Jul-2020
https://doi.org/10.32628/CSEIT206418
HosseinzadehBendarkheili FMohammadiBaghmolaei RAhmadi A(2019)Product Quality Assessment using Opinion Mining in Persian Online Shopping2019 27th Iranian Conference on Electrical Engineering (ICEE)10.1109/IranianCEE.2019.8786611(1917-1921)Online publication date: Apr-2019
https://doi.org/10.1109/IranianCEE.2019.8786611
Show More Cited By

Index Terms

Opinion analysis for business intelligence applications
1. Applied computing
  1. Enterprise computing
    1. Business process management
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning settings

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Published In

cover image ACM Other conferences

OBI '08: Proceedings of the first international workshop on Ontology-supported business intelligence

October 2008

95 pages

ISBN:9781605582191

DOI:10.1145/1452567

Conference Chairs:
Alistair Duke
BT Research and Venturing, UK
,
Martin Hepp
University of the German Federal Armed Forces, Germany
,
Kalina Bontcheva
University of Sheffield, UK
,
Marc Vilain
MITRE Corp.

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2008

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Sixth Framework Programme

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OBI '08

OBI '08: First International Workshop on Ontology-supported Business Intelligence

October 27, 2008

Karlsruhe, Germany

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Cited By

Chatterjee DMukherjee AMukhopadhyay SPanday MPanigrahi PGoswami S(2021)A Survey on Sentiment AnalysisEmerging Technologies in Data Mining and Information Security10.1007/978-981-33-4367-2_26(259-271)Online publication date: 5-May-2021
https://doi.org/10.1007/978-981-33-4367-2_26
Gujjar J PH R P(2020)Opinion Mining for the Customer Feedback using TextBlobInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT206418(72-76)Online publication date: 15-Jul-2020
https://doi.org/10.32628/CSEIT206418
HosseinzadehBendarkheili FMohammadiBaghmolaei RAhmadi A(2019)Product Quality Assessment using Opinion Mining in Persian Online Shopping2019 27th Iranian Conference on Electrical Engineering (ICEE)10.1109/IranianCEE.2019.8786611(1917-1921)Online publication date: Apr-2019
https://doi.org/10.1109/IranianCEE.2019.8786611
Delaney SChan CSmith D(2018)Natural Language Processing for Productivity Metrics for Software Development Profiling in Enterprise ApplicationsProceedings of the 2018 Artificial Intelligence and Cloud Computing Conference10.1145/3299819.3299830(83-87)Online publication date: 21-Dec-2018
https://dl.acm.org/doi/10.1145/3299819.3299830
Ananthi Sheshasaayee Jayanthi R(2016)A Prototype Model for Deriving Social Media Intelligence Using Opinion Mining from Microblog DataProceedings of the International Conference on Data Engineering and Communication Technology10.1007/978-981-10-1675-2_76(773-781)Online publication date: 24-Aug-2016
https://doi.org/10.1007/978-981-10-1675-2_76
Polpinij JSrikanjanapert NWongsin C(2016)Concept-Based Sentiment Analysis for Opinion Texts with Multiple-LanguagesRecent Advances in Information and Communication Technology 201610.1007/978-3-319-40415-8_4(27-36)Online publication date: 12-Jun-2016
https://doi.org/10.1007/978-3-319-40415-8_4
Rady S(2015)A business intelligent technique for sentiment estimation by management sectors2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)10.1109/IntelCIS.2015.7397247(370-376)Online publication date: Dec-2015
https://doi.org/10.1109/IntelCIS.2015.7397247
Liebeskind CKotlerman LDagan I(2015)Text Categorization from category name in an industry-motivated scenarioLanguage Resources and Evaluation10.1007/s10579-015-9298-349:2(227-261)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s10579-015-9298-3
Banchs RPenagos C(2013)Mining User-Generated Content for Social Research and Other ApplicationsSmall and Medium Enterprises10.4018/978-1-4666-3886-0.ch098(1945-1979)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-3886-0.ch098
Banchs RPenagos C(2013)Mining User-Generated Content for Social Research and Other ApplicationsEmerging Applications of Natural Language Processing10.4018/978-1-4666-2169-5.ch010(230-264)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-2169-5.ch010
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