research-article

Feature-based Facebook reviews process model for e-management using data mining

Authors:

Anish Kumar Varudharajulu,

Yongsheng MaAuthors Info & Claims

IC4E '19: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning

Pages 406 - 410

https://doi.org/10.1145/3306500.3306514

Published: 10 January 2019 Publication History

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Abstract

The data generated from online communication acts as potential gold mines for discovering knowledge for researchers. A large amount of data is also generated in the form of web documents, emails, blogs, and feedback, etc. Text analytics is being significantly employed to mine important information. Opinion mining is the process of extracting human thoughts and perceptions from unstructured texts. The showstopper for designing an opinion mining system for analyzing reviews arise from the fact that customer reviews are often noisy. These reviews are informally written. In addition, they are subjected to spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on analyzing the different classification and clustering algorithms aimed at extracting and consolidating opinions of customers from social media sites like Facebook, Twitter and through surveys, at multiple levels of granularity to monitor and measure customer satisfaction. Ours is an automated approach, in which the system aids in the process of knowledge assimilation for knowledge-based building and also performs the analytics. Domain experts ratify the knowledge base and also provide training datasets for the system to intuitively gather more instances for ratification. The system identifies opinion expressions as phrases containing opinion words, opinionated features and also opinion modifiers. These expressions are categorized as positive, negative or neutral. Opinion expressions are identified and categorized using localized linguistic techniques. Opinions can be congregated at any desired level of specificity i.e. feature level or product level, user level or service level, etc. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions crawled from a set of pre-defined datasets.

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Cited By

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Tan QHe FTeng L(2022)Using Baidu Index to Investigate the Spatiotemporal Characteristics of Knowledge Management in ChinaScientific Bulletin of Mukachevo State University Series “Economics”10.52566/msu-econ.9(2).2022.31-389:2(31-38)Online publication date: 2022
https://doi.org/10.52566/msu-econ.9(2).2022.31-38
Xie M(2022)Supply Chain Construction and Optimization Model Based on Grid Computing and Process Data Mining Algorithms2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)10.1109/ICIRCA54612.2022.9985711(1008-1012)Online publication date: 21-Sep-2022
https://doi.org/10.1109/ICIRCA54612.2022.9985711
Alhaj MLiu HSulaiman M(2022)Towards Occupant-Centric Facility Maintenance Management: Automated Classification of Occupant Feedback Using NLPProceedings of the Canadian Society of Civil Engineering Annual Conference 202110.1007/978-981-19-0968-9_24(297-307)Online publication date: 26-May-2022
https://doi.org/10.1007/978-981-19-0968-9_24
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Index Terms

Feature-based Facebook reviews process model for e-management using data mining
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering

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Published In

IC4E '19: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning

January 2019

469 pages

ISBN:9781450366021

DOI:10.1145/3306500

Conference Chairs:
Kuan-Chou Chen
Purdue University Northwest
,
Yongsheng Ma
University of Alberta, Canada

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

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Published: 10 January 2019

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NSERC Discovery grant
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IC4E 2019

IC4E 2019: 2019 10th International Conference on E-Education, E-Business, E-Management and E-Learning

January 10 - 13, 2019

Tokyo, Japan

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Tan QHe FTeng L(2022)Using Baidu Index to Investigate the Spatiotemporal Characteristics of Knowledge Management in ChinaScientific Bulletin of Mukachevo State University Series “Economics”10.52566/msu-econ.9(2).2022.31-389:2(31-38)Online publication date: 2022
https://doi.org/10.52566/msu-econ.9(2).2022.31-38
Xie M(2022)Supply Chain Construction and Optimization Model Based on Grid Computing and Process Data Mining Algorithms2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA)10.1109/ICIRCA54612.2022.9985711(1008-1012)Online publication date: 21-Sep-2022
https://doi.org/10.1109/ICIRCA54612.2022.9985711
Alhaj MLiu HSulaiman M(2022)Towards Occupant-Centric Facility Maintenance Management: Automated Classification of Occupant Feedback Using NLPProceedings of the Canadian Society of Civil Engineering Annual Conference 202110.1007/978-981-19-0968-9_24(297-307)Online publication date: 26-May-2022
https://doi.org/10.1007/978-981-19-0968-9_24
Dias de Almeida JDurão F(2020)Personalizing the Top-k Spatial Keyword Preference Query with textual classifiersExpert Systems with Applications10.1016/j.eswa.2020.113841162(113841)Online publication date: Dec-2020
https://doi.org/10.1016/j.eswa.2020.113841

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Ontology based feature level opinion mining for portuguese reviews

Extracting Collective Trends from Twitter Using Social-Based Data Mining

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