The DOI matches its Crossref record; 18 of the 19 references it cites were found in a registry or an index.
Sentiment analysis on tweets for social events
Abstract
Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of people's opinions. Tweets data can be efficiently used to infer people's opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people's opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.
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“The automation of sentiment detection on these social networks has gained attention for various purposes [18][19][20].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [19]
Citations
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Tooltip Translator for Social Networking
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Using Machine Learning to Analyze Sudanese Opinions for Political Decision Making
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Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis
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Automatic analysis of X (Twitter) data for supporting depression diagnosis
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A Machine Learning Model for Sentiment Classification of Mobile Products Data
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“In the same direction, Duwairi & Qarqaz [19] collected about 2591 Arabic tweets on topics such as education, sports, and political news.”
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Utilizing Social Media for Understanding Public Opinion on Transportation in Indian Cities
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Financial sentiment analysis meets algorithmic trading: a performance-based approach
Burchi, A., & Regni, L. (2026) · Cogent Economics & Finance · doi:10.1080/23322039.2026.2703376
Khan, A., Mir, A. A., Gul, S., Anoop, V. S., Naseema, S., Ahmad, S., & Khan, T. I. (2026) · Cogent Social Sciences · doi:10.1080/23311886.2026.2749157
Metadata
- Authors
- Zhou, X., Tao, X., Yong, J., & Yang, Z.
- Affiliations
- Queensland University of Technology (AU)
- University of Southern Queensland (AU)
- Aalborg University (DK)
- Venue
- Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 557-562
- OpenAlex
- W1990239322
- Cited by
- 124
- References
- 19
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 7 · 2023: 11 · 2022: 15 · 2021: 13 · 2020: 11 · 2019: 14 · 2018: 11 · 2017: 8 · 2016: 10 · 2015: 8 · 2014: 7
- FWCI
- 5.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer Supported Cooperative Work in Design (CSCWD) (publishedVersion)
- University of Southern Queensland ePrints (University of Southern Queensland) (acceptedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- sentiment analysis, Twitter, tweets, social gatherings, political attitudes, political candidates, Julia Gillard, Tony Abbott, public opinion
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