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 social networks has gained attention for various purposes.
“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]
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A novel intuitionistic fuzzy inference system for sentiment analysis
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Jagini, A., Mahajan, K., Aluvathingal, N., Mohan, V., & Prajwala, T. R. (2023) · International Conference on Smart Data Intelligence (ICSMDI) · doi:10.1109/icsmdi57622.2023.00015 · Cited by 7 · see citing works
An Ensemble-Based Decision Tree Approach for Educational Data Mining
Abdar, M., Moghadam, M. Z., & Zhou, X. (2018) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc.2018.8697318 · Cited by 7 · see citing works
“Data mining and machine learning algorithms have been widely used in range of research fields such as healthcare [1-4], medicine [57], economics [8, 9], decision support systems [10, 11], sentiment analysis [12-14] etc.”
MethodSelf-citationA Sentiment Delivering Estimate Scheme Based on Trust Chain in Mobile Social Network
Li, M., Xiang, Y., Zhang, B., & Huang, Z. (2015) · Mobile Information Systems · doi:10.1155/2015/745095 · Cited by 7 · see citing works
“In mobile social network, sentiment has been extracted for further analyzing in different platforms, such as Twitter [23, 30, 31], MySpace [32], Facebook [33], YouTube [34], and other platforms [26, 35, 36].”
BackgroundAthPPA: A Data Visualization Tool for Identifying Political Popularity over Twitter
Britzolakis, A., Kondylakis, H., & Papadakis, N. K. (2021) · Information · doi:10.3390/info12080312 · Cited by 6 · see citing works
“[45] focused on the Australian federal election 2010, trying to capture the sentiment of the specific political candidates.”
BackgroundAntil, A., & Verma, H. V. (2019) · Global Business Review · doi:10.1177/0972150919833514 · Cited by 6 · see citing works
“In the political context, Zhou et al. (2013) analysed the sentiments of tweets made in campaigning for Australian elections.”
BackgroundAn Emotion based Sentiment Analysis on Twitter Dataset
Sahu, L., & Shah, B. L. (2022) · IEEE International Conference on Current Development in Engineering and Technology (CCET) · doi:10.1109/ccet56606.2022.10079995 · Cited by 5 · see citing works
“The categorical emotion labels are forecasted using support vector machines, which foresee the likelihood that an input belongs to a certain class (Happy, Sad, Anger, Fear, Surprise, and Disgust)[15].”
BackgroundECO v1: Towards Event-Centric Opinion Mining
Xu, R. F., Lin, H., Liao, M., Han, X., Xu, J., Tan, W., Sun, Y., & Sun, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.216 · Cited by 5 · see citing works
Enhancing Event-Level Sentiment Analysis with Structured Arguments
Zhang, Q., Zhou, J., Chen, Q., Bai, Q.-C., & He, L.-B. (2022) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3477495.3531784 · Cited by 7 · see citing works
“There are some works namely event-based sentiment analysis [6, 9, 10, 19, 22, 23, 41], which are different from our task.”
BackgroundQuantifying the Effect of Sentiment on Topic Evolution in Chinese Microblog
Fu, P., Lin, Z., Lin, H., Yuan, F., Wang, W., & Meng, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45814-4_43 · Cited by 5 · see citing works
Khatoon, M., Banu, W. A., Zohra, A. A., & Chinthamani, S. A. M. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-8848-3_70 · Cited by 5 · see citing works
Social Media Chatbot System - Beekeeping Case Study
Latioui, Z. E., Bougueroua, L., & Moretto, A. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-14347-3_29 · Cited by 5 · see citing works
A Smart Cyber-Human System to Support Mental Well-Being through Social Engagement
Majumder, A. J. A., Dedmondt, J. W., Jones, S., & Asif, A. A. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.0-134 · Cited by 5 · see citing works
“The automation of sentiment detection on these social media has gained attention for various purposes [30-32].”
BackgroundThakur, D., Hankerson, D. L., Luria, M., Savage, S., Rodriguez, M., & Valdovinos, M. G. (2022) · doi:10.31219/osf.io/bwta3 · Cited by 5 · see citing works
Sentiment analysis using sentence minimization with natural language generation (NLG)
Likhar, M., & Kasar, S. L. (2017) · International Conference on Intelligent Systems and Information Management (ICISIM) · doi:10.1109/icisim.2017.8122163 · Cited by 4 · see citing works
Temporal Sentiment Tracking and Analysis on Large-scale Social Events
Hazimeh, H., Harissa, M., Mugellini, E., & Khaled, O. A. (2019) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3316615.3316646 · Cited by 4 · see citing works
“Michael Brooks and Others in [4, 5] designed and developed a new tool named Agave to support collaborative exploration of events in large tweets data set, especially in terms of sentiment.”
BackgroundInvestigating the Lack of Consensus Among Sentiment Analysis Tools
Palomino, M. A., Varma, A. P., Bedala, G. K., & Connelly, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66527-2_5 · Cited by 4 · see citing works
ATAM: Arabic Traffic Analysis Model for Twitter
AlFarasani, A., AlHarthi, T., & Alhumoud, S. O. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100343 · Cited by 4 · see citing works
“[18] collected a dataset containing 57,000 tweets in the form of 1,000 tweets split into 57 files.”
BackgroundReal-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis
Hiriyannaiah, S., Siddesh, G. M., & Srinivasa, K. G. (2018) · International Journal of Information Technology and Web Engineering · doi:10.4018/ijitwe.2018070107 · Cited by 4 · see citing works
Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey
Leenuse, M. L., Pankaj, D. S., & Rahul Raj, M. (2023) · International Conference on Control, Communication and Computing (ICCC) · doi:10.1109/iccc57789.2023.10165097 · Cited by 3 · see citing works
“The popular method used for the same is sentiment analysis [21], through which the most accurate prediction can always be assured.”
MethodKumar, A., Dutt, V., García‐Díaz, V., & Narang, S. K. (2021) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v10i5.3078 · Cited by 3 · see citing works
“Tweet textsentiment miningmodel consisted of the preprocessing, feature selection, and identification modules [8].”
MethodMultilingual Sentiment Mining System to Prognosticate Governance
Zahedi, R. K., Alajlan, N., Zahedi, H. K., & Rabczuk, T. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.021384 · Cited by 3 · see citing works
Analysis of data retrieval and opinion mining system
Wani, S. S., & Patil, Y. N. (2015) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2015.7154793 · Cited by 3 · see citing works
“Opinion mining is an important type of text analysis which support in decision making by extracting and analysing text, identifying positive and negative opinions [6].”
BackgroundMinimizing Negative Influence in Social Networks: A Graph OLAP Based Approach
Challal, Z., Boussaïd, O., & Boukhalfa, K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-64471-4_30 · Cited by 3 · see citing works
Alves, D. S. (2015) · doi:10.26512/2015.10.t.19345 · Cited by 3 · see citing works
“Outro trabalho também focado em questões sociais foi realizado por [67].”
BackgroundDeep learning of latent textual structures for the normalization of Arabic writing
Fadili, H. (2020) · International Multi-Conference on Organization of Knowledge and Advanced Technologies (OCTA) · doi:10.1109/octa49274.2020.9151846 · Cited by 2 · see citing works
SENTIMENT ANALYSIS OF COVID-19 TWEETS
Nureni, A. A., Ogunlusi, V. E., & Uloko, E. J. (2021) · FUDMA Journal of Sciences · doi:10.33003/fjs-2021-0501-690 · Cited by 2 · see citing works
Hegde, R., & Seema, S. (2018) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2018.8493771 · Cited by 2 · see citing works
“User opinions are retrieved [6].”
Facebook Reactions as Controversy Proxies: Predictive Models over Italian News
Basile, A., Caselli, T., Merenda, F., & Nissim, M. (2018) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.514 · Cited by 2 · see citing works
“Works in the areas of Sentiment Analysis (Zhou et al. 2013; Deng and Wiebe 2015; Deng, Choi, and Wiebe 2013; Chambers et al. 2015; Russo, Caselli, and Strapparava 2015), Emotion Detection (Strapparava and Mihalcea 2007, 2008; Russo et al.”
BackgroundMining Sports Articles using Cuckoo Search and Tabu Search with SMOTE Preprocessing Technique
Almayyan, W. I. (2021) · Journal of Computer Science · doi:10.3844/jcssp.2021.231.241 · Cited by 2 · see citing works
“This includes online product or service reviews, travel advice, social media discussions and blogs, customer recommendations, movie and book reviews and stock market predictions (Zhou et al ., 2013).”
BackgroundHan, S., Ji, H., Jung, J., Lee, U., & Liu, M. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09902-1 · Cited by 1 · see citing works
KHẢO SÁT CÁC MÔ HÌNH PHÂN LOẠI VĂN BẢN TIẾNG VIỆT
HIẾU, N. C. (2022) · Journal of Science and Technology - IUH · doi:10.46242/jstiuh.v57i03.4395 · Cited by 1 · see citing works
Exploring Concept Drift in Technology by Tweets Mining
Naci, M., & Durmuşoğlu, A. (2023) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/ap03.20220477 · Cited by 1 · see citing works
Twitter sentimental analysis using machine learning
Dhanta, R., Sharma, H., Kumar, V., & Singh, H. O. (2023) · International Journal of Communication and Information Technology · doi:10.33545/2707661x.2023.v4.i1a.63 · Cited by 1 · see citing works
“You may categorise tweets into ~ 82 ~ positive, negative, or neutral sentiment groups by utilising machine learning methods like Naive Bayes, Support Vector Machines, and Recurrent Neural Networks.”
InfluentialKacmarova, K., McPhail, H., Kothari, A. R., James, L., Foisey, L., Donelle, L., & Bauer, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-85908-3_14 · Cited by 1 · see citing works
Temporal Analysis of Comparative Opinion Mining
Varathan, K. D., Giachanou, A., & Crestani, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49304-6_36 · Cited by 1 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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