research-article

Automated Content Analysis: A Sentiment Analysis on Malaysian Government Social Media

Authors:

Siti Salwa Hasbullah,

Rita Zaharah Wan Chik,

Farahwahida Mohd,

Myzan NoorAuthors Info & Claims

IMCOM '16: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication

Article No.: 30, Pages 1 - 6

https://doi.org/10.1145/2857546.2857577

Published: 04 January 2016 Publication History

Abstract

Social media has become the current trend in information technology industry that acts as the collective of online communication channels for community-based input, interaction, content-sharing and collaboration, used all around the world. Microblogging platforms such as Facebook and Twitter are fast communication channels for information sharing among worldwide users. An automated content analysis tool is proposed in this paper to help Malaysian legal firms and the Malaysian official leaders understand public sentiment via their comments on official Malaysian government leaders' social media sites such as Twitter and Facebook. In this paper, we explore and apply Semantic Role Labeling (SRL) techniques that generate new methods to filter and classify the content data set, advancing the state of the art in sentiment detection approaches, which currently suffer from lack of accuracy. In particular, semantic role labeling techniques have been little studied for use on degraded social media text, which causes numerous problems for traditional Natural Language Processing (NLP) techniques. This work provides a platform for society, especially Malaysian government Legal Firms, IT Agencies and the public as a whole, to measure the impact of public sentiment over Malaysian government officials for policy making and the future development in Malaysia. The work is novel in its application of SRL techniques to social media, and in the use of SRL for improving the quality of sentiment analysis.

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https://doi.org/10.4018/979-8-3693-0418-1.ch019
Mir TLawaye AKhanday A(2023)NLP Techniques and Challenges to Process Social Media DataAdvanced Applications of NLP and Deep Learning in Social Media Data10.4018/978-1-6684-6909-5.ch009(171-218)Online publication date: 9-Jun-2023
https://doi.org/10.4018/978-1-6684-6909-5.ch009
Jiang YPang PWong DKan H(2023)Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic ReviewApplied Sciences10.3390/app13221234613:22(12346)Online publication date: 15-Nov-2023
https://doi.org/10.3390/app132212346
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Index Terms

Automated Content Analysis: A Sentiment Analysis on Malaysian Government Social Media
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Conferences

IMCOM '16: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication

January 2016

658 pages

ISBN:9781450341424

DOI:10.1145/2857546

Copyright © 2016 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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Published: 04 January 2016

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IMCOM '16: The 10th International Conference on Ubiquitous Information Management and Communication

January 4 - 6, 2016

Danang, Viet Nam

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Overall Acceptance Rate 213 of 621 submissions, 34%

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https://doi.org/10.4018/979-8-3693-0418-1.ch019
Mir TLawaye AKhanday A(2023)NLP Techniques and Challenges to Process Social Media DataAdvanced Applications of NLP and Deep Learning in Social Media Data10.4018/978-1-6684-6909-5.ch009(171-218)Online publication date: 9-Jun-2023
https://doi.org/10.4018/978-1-6684-6909-5.ch009
Jiang YPang PWong DKan H(2023)Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic ReviewApplied Sciences10.3390/app13221234613:22(12346)Online publication date: 15-Nov-2023
https://doi.org/10.3390/app132212346
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