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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]
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Alharbi, A. S. M., & de Doncker, E. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.10.001 · Cited by 228
Sentiment analysis and opinion mining on educational data: A survey
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2022.100003 · Cited by 215
“The analysis of these sources to extract sentiment or opinions of users started a decade ago [3, 4].”
BackgroundSelf-citationMonitoring the public opinion about the vaccination topic from tweets analysis
D’Andrea, E., Ducange, P., Bechini, A., Renda, A., & Marcelloni, F. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.009 · Cited by 169
“, 2011) and the outcome of political elections (Zhou et al., 2013).”
BackgroundSurvey on AI-Based Multimodal Methods for Emotion Detection
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16272-6_11 · Cited by 137
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Georgiadou, E., Angelopoulos, S. P., & Drake, H. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.102048 · Cited by 116
Bitcoin price change and trend prediction through twitter sentiment and data volume
Critien, J. V., Albert Gatt, A., & Ellul, J. (2022) · Financial Innovation · doi:10.1186/s40854-022-00352-7 · Cited by 98
“In addition, it could very well be the case that people tend to tweet more positively than negatively as seen in Pantano et al. (2018), Zhou et al. (2013), Abraham et al. (2018) and Kraaijeveld and De Smedt (2020).”
BackgroundLi, D., Chaudhary, H., & Zhang, Z. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17144988 · Cited by 94
Social big data mining: A survey focused on opinion mining and sentiments analysis
Guellil, I., & Boukhalfa, K. (2015) · International Symposium on Programming and Systems (ISPS) · doi:10.1109/isps.2015.7244976 · Cited by 87
“For the aspect level, we can mention the approaches proposed by [41,47,50,54,60].”
BackgroundMethodInfluentialA novel sentiment analysis of social networks using supervised learning
Anjaria, M., & Guddeti, R. M. R. (2014) · Social Network Analysis and Mining · doi:10.1007/s13278-014-0181-9 · Cited by 83
Younis, E. M. G. (2015) · Cited by 80
Sentiment Analysis Using Lexicon and Machine Learning-Based Approaches: A Survey
Verma, B., & Thakur, R. S. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-10-8198-9_46 · Cited by 68
Influential user weighted sentiment analysis on topic based microblogging community
Eliaçik, A. B., & Erdoğan, N. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.10.006 · Cited by 66
Are Black Friday Deals Worth It? Mining Twitter Users’ Sentiment and Behavior Response
Saura, J. R., Reyes-Menéndez, A., & Palos‐Sánchez, P. R. (2019) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc5030058 · Cited by 60
Using Twitter data to predict the performance of Bollywood movies
Gaikar, D. D., Marakarkandy, B., & Dasgupta, C. (2015) · Industrial Management & Data Systems · doi:10.1108/imds-04-2015-0145 · Cited by 58
Neural networks for sentiment analysis on Twitter
Duncan, B., & Zhang, Y. (2015) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/icci-cc.2015.7259397 · Cited by 57
“Sentiment analysis has been useful for companies to get their customer's opinions on their products [4], [5], [2], predicting outcomes of elections [6], and getting opinions from movie reviews [3], [4].”
BackgroundAnalysis of Various Sentiment Classification Techniques
Vimalkumar, B., & Bhumika, M. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016909259 · Cited by 56
“Opinion words are extracted using Wilson lexicon list [12].”
BackgroundMethodSentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis
Bhumika, M., & Vimalkumar, B. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016910921 · Cited by 52
“It is used to predict prior public opinion [8].”
Convolutional Neural Network based sentiment analysis using Adaboost combination
Gao, Y., Rong, W., Shen, Y., & Xiong, Z. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727352 · Cited by 46
“…polarities, sentiment analysis has achieved increasing popularity among scientific and industrial research and has been widely used to predict people’s attitudes in different domains, e.g., product recommendation [2], social event identification [3], political election prediction [4], and etc.”
BackgroundOnline learning sentiment analysis during the covid-19 Indonesia pandemic using twitter data
Sahir, S. H., Ramadhana, R. S. A., Marpaung, M. F. R., Munthe, S. R., & Watrianthos, R. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1156/1/012011 · Cited by 44
“The process of collecting data in text mining can produce a sensing analysis that identifies an emotionally positive or negative statement [14].”
BackgroundArabic ChatGPT Tweets Classification Using RoBERTa and BERT Ensemble Model
Mujahid, M., Kanwal, K., Rustam, F., Aljedaani, W., & Ashraf, I. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3605889 · Cited by 50
“Similarly, [51] presented a tweets sentiment analysis model (TSAM) which is based on the lexicon technique.”
MethodPrediction and analysis of Pakistan election 2013 based on sentiment analysis
Razzaq, M. A., Qamar, A. M., & Bilal, H. S. M. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921662 · Cited by 44
Sentiment Analysis of Political Tweets From the 2019 Spanish Elections
Rodríguez-Ibáñez, M., Gimeno-Blanes, F.-J., Cuenca-Jiménez, P.-M., Soguero-Ruíz, C., & Rojo‐Álvarez, J. L. (2021) · IEEE Access · doi:10.1109/access.2021.3097492 · Cited by 46
“Finally, in [43], a Tweets Sentiment Analysis Model was proposed that could capture societal interest and people’s opinions toward a specific event; the study demonstrated that building a lexicon-based sentiment analysis intelligent system could be very beneficial.”
BackgroundFramework for Sentiment-Driven Evaluation of Customer Satisfaction With Cosmetics Brands
Park, J. (2020) · IEEE Access · doi:10.1109/access.2020.2997522 · Cited by 38
“The most often employed methods for supervised learning are based on vector machines SVM [53], [54], Bayesian net-”
MethodA survey on text classification and its applications
Zhou, X., Gururajan, R., Li, Y., Venkataraman, R., Tao, X., Bargshady, G., Barua, P. D., & Kondalsamy‐Chennakesavan, S. (2020) · Web Intelligence · doi:10.3233/web-200442 · Cited by 41
“…of incoming e-mail message and memos, the classification of Web pages, classification of academic papers using technical domains and sub-domains, spam and porn filtering, bioinformatics, customer service automation, topic categorize and sentiment analysis etc. [12,20,48,70,75,86,89,99,100].”
Self-citationHigh-Performance Modelling and Simulation for Big Data Applications
Kołodziej, J., & González–Vélez, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6 · Cited by 37
EDSA-Ensemble: An Event Detection Sentiment Analysis Ensemble Architecture
Petrescu, A., Truică, C., Apostol, E., & Paschke, A. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3434355 · Cited by 36
“For the Sentiment Analysis task, we are using the standard architectures for the chosen methods.”
Divyapushpalakshmi, M., & Ramalakshmi, R. (2021) · International Journal of Speech Technology · doi:10.1007/s10772-021-09801-7 · Cited by 33
“Though sentiment analysis has various applications, movie review and customer review analysis to find a better product and recommendation has been the key focus areas (Zhou et al. 2013).”
BackgroundOpinion mining and analysis: A literature review
Singh, V., & Dubey, S. K. (2014) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence.2014.6949318 · Cited by 31
“In direct opinion the statement is direct and the sentiments are independent from other object [7].”
BackgroundOpinion Mining for the Tweets in Healthcare Sector using Fuzzy Association Rule
Mittal, M., Kaur, I., Pandey, S. C., Verma, A., & Goyal, L. M. (2018) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eai.13-7-2018.159861 · Cited by 30
Automatic Unsupervised Polarity Detection on a Twitter Data Stream
Terrana, D., Augello, A., & Pilato, G. (2014) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2014.17 · Cited by 29
Coupling topic modelling in opinion mining for social media analysis
Zhou, X., Tao, X., Rahman, M. M., & Zhang, J. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3106459 · Cited by 29
“1, at the rst stage, the Tweet Sentiment Analysis Model (TSAM) developed by [23] will be used to sentiment calculation and scoring for the entity.”
BackgroundMethodInfluentialSelf-citationArabic Tweets Sentiment Analysis about Online Learning during COVID-19 in Saudi Arabia
Althagafi, A., Althobaiti, G., Alhakami, H., & Alsubait, T. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120373 · Cited by 29
“[12] present a study about a Tweets Sentiment Analysis Model (TSAM).”
BackgroundSentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies
Qamar, A. M., Suliman, A., & Sohail, S. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080150 · Cited by 29
“[8] developed a Tweet Sentiment Analysis Model (TSAM) which was able to successfully determine the societal interest as well as general peoples’ opinions with respect to a social event (Australian federal elections).”
BackgroundPrediction and analysis of Pakistan election 2013 based on sentiment analysis
Razzaq, M. A., Qamar, A. M., & Bilal, H. S. M. (2014) · Advances in Social Networks Analysis and Mining · doi:10.5555/3191835.3191972 · Cited by 28
Untawale, T. M., & Choudhari, G. (2019) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819800 · Cited by 27
“Author proposes a Tweets Sentiment Analysis Model (TSAM) [8] that can spot the social interest and general people's opinions in a social event.”
BackgroundSamsir, S., Ambiyar, A., Verawardina, U., Edi, F., & Watrianthos, R. (2021) · JURNAL MEDIA INFORMATIKA BUDIDARMA · doi:10.30865/mib.v5i1.2580 · Cited by 26
“Proses pengambilan informasi dalam teks mining dapat menghasilkan analisis perasaan yang secara emosional mengidentifikasi pernyataan jika positif atau negative [23].”
BackgroundSentiment Analysis Using Lexicon Based Approach
Singh, V. P., Singh, G., Rastogi, P., & Deswal, D. (2018) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc.2018.8745971 · Cited by 23
“The system performance is measured with its accuracy [7].”
BackgroundMethodResultInfluentialA, S. B., & N, R. P. K. (2023) · Journal of Machine and Computing · doi:10.53759/7669/jmc202303034 · Cited by 24
“To meet industrial demands, automated sentiment analysis technologies have evolved by using AI and NLP techniques [1-4][6-9].”
MethodPredicting Controversial News Using Facebook Reactions
Basile, A., Caselli, T., & Nissim, M. (2017) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.2370 · Cited by 22
“In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pages 153–160.”
BackgroundInfluentialSentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm
Mahrukh, R., Shakil, S., & Malik, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-33734-7 · Cited by 19
Aziz, A., Chowdhury, N. K., Kabir, M. A., Chy, A. N., & Siddique, M. J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129376 · Cited by 25
Data Analytics on Online Student Engagement Data for Academic Performance Modeling
Tao, X., Shannon-Honson, A., Delaney, P., Li, L., Dann, C. E., Li, Y., & Xie, H. (2022) · IEEE Access · doi:10.1109/access.2022.3208953 · Cited by 22
“Data-mining techniques are well-established in Social Media 114 research for retrieving textual content to model user behav- 115 ior [11], [12], [13], [14], [15].”
MethodSelf-citationReal time road traffic monitoring alert based on incremental learning from tweets
Wang, D., Alrubaie, A., Davies, J., & Clarke, S. S. (2014) · IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) · doi:10.1109/eals.2014.7009503 · Cited by 21
“In general, due to the nature of tweets (short messages using very informal language with many abbreviations), text mining techniques (as used by [4-11]) achieve accuracy in detecting traffic-relevant tweets of 40% to 80%.”
MethodXin, Y., & MacEachren, A. M. (2020) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2020.1770259 · Cited by 20
Gjorshoska, I., Dedinec, A., Prodanova, J., Dedinec, A., & Kocarev, L. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102130 · Cited by 20
Bibi, A., & Azim, T. (2019) · IEEE Access · doi:10.1109/access.2019.2940528 · Cited by 20
“Political web linking and sentiment analysis has also been explored for the US Congress elections [25], [26], Australian federal elections of 2010, where political candidates sentiment was analysed [27] and for the general elections of 2013 in Pakistan [28], where the analytical findings from machine learning techniqu…”
ResultNon-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts
Diaz-Garcia, J. A., Ruiz, M. D., & Martín-Bautista, M. J. (2020) · IEEE Access · doi:10.1109/access.2020.2990461 · Cited by 20
“Regarding the fields of sentiment analysis and association rules, there are few related studies due to the predominance of classification methods [41], [42] in these areas.”
BackgroundShafana, A. R. F., & Safnas, S. M. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00899-4 · Cited by 20
“Thus, it can be concluded that Sentiment Analysis on social media data, especially on Twitter data has provided sufficient insights on the target context since the past (Agarwal et al. 2011; Budiharto & Meiliana 2018; Paltoglou et al. 2010; Paltoglou & Giachanou 2014; Zhou et al. 2013).”
BackgroundA data-driven kinetic model for opinion dynamics with social network contacts
Albi, G., Calzola, E., & Dimarco, G. (2024) · European Journal of Applied Mathematics · doi:10.1017/s0956792524000068 · Cited by 19
“We mention that SA, also known as Opinion Mining is a subclass of Natural Language Process (NLP) methods to analyze textual information, in this context we refer to [53, 43, 54, 39] for further details.”
Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO
Hafidz, N., & Liliana, D. Y. (2021) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v5i2.2960 · Cited by 17
“dengan mengekstrak dan menganalisis teks, serta mengidentifikasi opini-opini positif dan negatif [17], [18].”
BackgroundAnalysis of product Twitter data though opinion mining
Fernandes, R., & D’Souza, R. G. L. (2016) · IEEE India Conference · doi:10.1109/indicon.2016.7839025 · Cited by 15
“Xujuan Zhou, Xiaohui Tao, Jianming Yong, Zhenyu Yang [7], discuss about the tool named TSAM (Tweets Sentiment Analysis Model).”
MethodReverse spatial top-k keyword queries
Ahmed, P., Eldawy, A., Hristidis, V., & Tsotras, V. J. (2022) · The VLDB Journal · doi:10.1007/s00778-022-00759-9 · Cited by 12
“There are several works that study how to efficiently select posts based on various criteria like location [40], time [3,40], keywords [3,38,40], sentiment [55,57], or topic [38,40].”
BackgroundSocial Media Analysis: A Tool for Popularity Prediction Using Machine Learning Classifiers
Goel, S., Monica, Khurana, H., & Jain, P. (2022) · Women in engineering and science · doi:10.1007/978-3-031-17929-7_9 · Cited by 12
Domain Specific Lexicon Generation through Sentiment Analysis
Shaukat, K., Hameed, I. A., Luo, S., Javed, I., Iqbal, F., Faisal, A., Masood, R., Usman, A., Shaukat, U., Hassan, R., et al. (2020) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v15i09.13109 · Cited by 11
“Senticnet is an openly accessible semantic means which consists of frequent utilization of polarity ideas [20-22].”
BackgroundWee, C. K., Zhou, X., Sun, R., Gururajan, R., Tao, X., Li, Y., & Wee, N. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19127384 · Cited by 11
“This topic modelling approach is commonly used for AIbased social media analysis [15,16], but not for the clinical context where a high level of certainty is a must as it impacts public health and patient safety [17,18].”
MethodSelf-citationRetractedUtilizing the Twitter social media to identify transportation-related grievances in Indian cities
Pullanikkat, R., Poddar, S., Das, A., Jaiswal, T., Singh, V. K., Basu, M., & Ghosh, S. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01278-x · Cited by 10
“Twitter data has also been utilized for marketing and social studies, providing insights into people’s opinions on political, religious, and societal events (Zhou et al. 2013; Mandloi and Patel 2020).”
A novel intuitionistic fuzzy inference system for sentiment analysis
Dhyani, M., Kushwaha, G. S., & Kumar, S. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01014-8 · Cited by 10
“Sentiment analysis (SA) is a natural language processing technique that determines whether text is neutral, negative or positive [3].”
BackgroundEfficient sentiment classification of Twitter feeds
Chamansingh, N., & Hosein, P. A. (2016) · IEEE International Conference on Knowledge Engineering and Applications (ICKEA) · doi:10.1109/ickea.2016.7802996 · Cited by 9
“From the previous work as weil as work included in [3], [6], [7] and [8], one can conclude that the Naive Bayes clas sification model was the least computationally expensive and provided a reasonable accuracy when compared to Maximum Entropy and Support Vector Machine.”
MethodModeling Recommendation System for Real Time Analysis of Social Media Dynamics
Goel, S., Banthia, M., & Sinha, A. (2018) · International Conference on Contemporary Computing · doi:10.1109/ic3.2018.8530458 · Cited by 9
“The authors of [2] devised a model that takes people opinion regarding a particular social or political event as a key interest and analyze twitter data for the sentimental analysis of those tweets that whether the society likes a particular event or proposal or they hate it.”
BackgroundAccess to credit and fintech: A lexicon-based sentiment analysis application on Twitter data
Bredice, M., Formisano, A. V., Kullafi, S., & Palma, P. (2025) · Research in International Business and Finance · doi:10.1016/j.ribaf.2025.102875 · Cited by 8
Cuzzocrea, A., & Pilato, G. (2019) · Soft Computing · doi:10.1007/s00500-019-04478-2 · Cited by 8
“Zhou et al. (2013) propose a tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general people’s opinions in regard to Australian federal election 2010 event.”
BackgroundEvent identification and assertion from social media using auto-extendable knowledge base
Suliman, A., Kaabi, K. A., Wang, D., Alrubaie, A., Al-Dhanhani, A., Ruta, D., Davies, J., & Clarke, S. S. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727781 · Cited by 8
Monitoring negative opinion about vaccines from tweets analysis
D’Andrea, E., Ducange, P., & Marcelloni, F. (2017) · International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) · doi:10.1109/icrcicn.2017.8234504 · Cited by 8
“, i) to explain or predict events such as movements in stock markets [5], or the outcome of political elections [6], or ii) monitoring public health concerns [7], e.”
BackgroundElmokhtar, G., Mohamed, W. M., El-Shafeiy, E., & Younis, E. M. G. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11194-6 · Cited by 7
“They tested their system on five datasets: Sentiment Analysis 100,000 tweets [23], 1000 Tweets [24], Amazon Labeled Dataset [25], Yelp Dataset [26], and Amazon Mobiles Dataset [27], collected from different sources to evaluate the performance of their system.”
Twitter Sentiment Analysis for Bitcoin Price Prediction
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
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
“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
“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
“[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
“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
“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
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
“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
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
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
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
“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) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/bwta3 · Cited by 6
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
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
“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
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
“[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
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
“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
“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
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
“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
Alves, D. S. (2015) · RIUnB Institutional Repository (University of Brasília) · doi:10.26512/2015.10.t.19345 · Cited by 3
“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
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
Hegde, R., & Seema, S. (2018) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt.2018.8493771 · Cited by 2
“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
“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
“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
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
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
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
“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
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
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
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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