Abstract
Twitter boasts 319 million daily active users, making it an invaluable asset for public figures and businesses looking to cultivate positive public image. Businesses can leverage sentiment analysis for real-time polling on various social media platforms allowing them to gauge public sentiment and opinion accurately. Recently, academic researchers focused on sentiment analysis as an approach for Twitter propaganda analysis. Text sentiment analysis is an automated process that offers valuable insight into the content of a text segment. It can reveal whether it conveys factual or subjective information and reveal its polarity; for Twitter sentiment classification this goal primarily lies with determining whether tweets have positive or negative undertones; researchers utilize various Machine Learning (ML), Deep Learning (DL) and other models to accomplish this task. Present research work utilising classification algorithms such as Support Vector Machines are among the most frequently utilized ML/DL models for sentiment analysis, along with Random Forest, Ensemble Machine Learning, Artificial Neural Networks, Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) for effective classification. Also, for preprocessing the tweets API, techniques such as filtering, tokenization, removal of stopwords, stemming and lemmatization have been used. Then preprocessed input is fed as input to the TF-IDF and Bag of Words for vectorize the input. Then classification has been performed with aforementioned models. Finally, performance evaluation metrices have been perfomed, from that out of all these models used for sentiment analysis on Twitter dataset, Bidirectional LSTM has proven itself most accurate at detecting sentiment with an accuracy rate of 98.14%, 98.39% in vectorize techniques includes TF-IDF and Bag of Words—making this tool invaluable when conducting voice analyses on this platform.
Similar content being viewed by others
Data availability
Not Applicable
Code availability
Not Applicable
References
Surnar A, Sonawane S (2017) Review for twitter sentiment analysis using various methods. Int J Adv Res Comput 6(05):2278–1323
Eliacik AB, Erdoğan N (2015) User-weighted sentiment analysis for financial community on Twitter. In: 2015 11th International Conference on Innovations in Information Technology (IIT). IEEE, pp 46–51
Ahmed K, El Tazi N, Hossny AH (2015) Sentiment analysis over social networks: an overview. In: 2015 IEEE international conference on systems, man, and cybernetics. IEEE, pp 2174–2179
Ko Y, Seo J (2000) Automatic text categorization by unsupervised learning. In: COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics
Kharche SR, Lokesh B (2015) Review on sentiment analysis of twitter data. Int J Comput Sci Appl 8
Gupta B et al (2017) Study of twitter sentiment analysis using machine learning algorithms on python. Int J Comput Appl 165(9):29–34
Jagdale RS, Shirsat VS, Deshmukh SN (2016) Sentiment analysis of events from Twitter using open source tool. Int J Comput Sci Mob Computing 5(4):475–485
Medhat W et al (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5:1093–1113
Sharma R, Nigam S, Jain R (2014) Opinion mining of movie reviews at document level. arXiv preprint arXiv:1408.3829
Moraes R, Valiati JF, GaviãO Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621–633
Bhatia P, Ji Y, Eisenstein J (2015) Better document-level sentiment analysis from rst discourse parsing. arXiv preprint arXiv:1509.01599
Tu Z et al (2012) Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics
Hurst MF, Nigam K (2003) Retrieving topical sentiments from online document collections. In: Document Recognition and Retrieval XI, vol 5296. SPIE, pp 27–34
Lin WH, Wilson T, Wiebe J, Hauptmann AG (2006) Which side are you on? Identifying perspectives at the document and sentence levels. In: Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X). CoNLL-X, pp 109–116
Yessenalina A, Yue Y, Cardie C (2010) Multi-level structured models for document-level sentiment classification. In: Proceedings of the 2010 conference on empirical methods in natural language processing, pp 1046–1056
Kumar S, Singh P, Rani S (2016) Sentimental analysis of social media using R language and Hadoop: Rhadoop, 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) 207–213. https://doi.org/10.1109/ICRITO.2016.7784953
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Liu B (2022) Sentiment analysis and opinion mining. Springer Nature
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2:1–135
Vinodhini G, Chandrasekaran RM (2012) Sentiment analysis and opinion mining: a survey. Int J 2(6):282–292.23
Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56(4):82–89
Raheja S, Asthana A (2021) Sentimental analysis of twitter comments on COVID-19. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, pp 704–708
Wang L, Gan JQ (2017) Prediction of the 2017 French election based on Twitter data analysis. In: 2017 9th Computer Science and Electronic Engineering (CEEC). IEEE, pp 89–93
Çeliktuğ MF (2018) Twitter sentiment analysis, 3-way classification: positive, negative or neutral? IEEE Int Conf Big Data (Big Data) 2018:2098–2103. https://doi.org/10.1109/BigData.2018.8621970
Subramaniam G, Aswini R, Ranjitha M, Rajendran PK (2017) Survey on user emotion analysis using Twitter data. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, pp 998–1001
Saitulasi K, Deepa N (2021) Deep belief network and sentimental analysis for extracting on multi-variable features to predict stock market performance and accuracy. International Conference on Computer Communication and Informatics (ICCCI) 2021:1–3. https://doi.org/10.1109/ICCCI50826.2021.9456999
Rathi M, Malik A, Varshney D, Sharma R, Mendiratta S (2018) Sentiment analysis of tweets using machine learning approach. 2018 Eleventh International Conference on Contemporary Computing (IC3), 1–3. https://doi.org/10.1109/IC3.2018.8530517
Alqarni A, Rahman A (2023) Arabic tweets-based sentiment analysis to investigate the impact of COVID-19 in KSA: a deep learning approach. Big Data Cogn Comput 7(1):16
Bibi M, Abbasi WA, Aziz W, Khalil S, Uddin M, Iwendi C, Gadekallu TR (2022) A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis. Pattern Recogn Lett 158:80–86
Jagadeesan M, Saravanan TM, Selvaraj PA, Asif Ali U, Arunsivaraj J, Balasubramanian S (2022) Twitter Sentiment Analysis with Machine Learning. In: 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, pp 681–686
Modak S, Mondal AC (2022) Sentiment analysis of twitter data using clustering and classification. In: Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021. Springer Nature, Singapore, pp 651–664
Rodrigues AP, Fernandes R, Shetty A, Lakshmanna K, Shafi RM (2022) Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques. Comput Intell Neurosci 2022
Vyas P, Reisslein M, Rimal BP, Vyas G, Basyal GP, Muzumdar P (2021) Automated classification of societal sentiments on Twitter with machine learning. IEEE Trans Technol Soc 3(2):100–110
Hsu D, Moh M, Moh T (2017) Mining frequency of drug side effects over a large twitter dataset using apache spark. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2017. IEEE, pp 915–924
Jianqiang Z, Xiaolin G, Xuejun Z (2018) Deep convolution neural networks for twitter sentiment analysis. IEEE Access 6:23253–23260
Sadr H, Pedram M, Teshnehlab M (2020) Multi-view deep network: a deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE Access 8:86984–86997
Stojanovski D, Strezoski G, Madjarov G et al (2018) Deep neural network architecture for sentiment analysis and emotion identification of twitter messages. Multimed Tools Appl 77(24):32213–32242
Yang X, Xu S, Wu H, Bie R (2019) Sentiment analysis of Weibo comment texts based on extended vocabulary and convolutional neural network. Procedia Comput Sci 147:361–368
Jain D, Kumar A, Garg G (2020) Sarcasm detection in mash-up language using soft-attention based bi-directional lstm and feature-rich cnn. Appl Soft Comput 91:106198
Wang M-D, Hu G-M (2020) A novel method for twitter sentiment analysis based on attentional-graph neural network. Information 11(2):92
Vijavakumar R, Ravikumar S, Vijay K, Sivaranjani P (2022) Integrated Communal Attentive & Warning System via Cellular Systems. In: 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC). IEEE, pp 370–375
Ling M-J, Chen Q-H, Sun Q, Jia Y-B (2020) Hybrid neural network for sina weibo sentiment analysis. IEEE Trans Comput Soc Syst 7(4):983–990
Kumar S, Yadava M, Roy P (2019) Fusion of eeg response and sentiment analysis of products review to predict customer satisfaction. Inf Fusion 52:41–52
Singh VK, Piryani R, Waila P, Devaraj M (2014) Computing sentiment polarity of texts at document and aspect levels. ECTI Transa Comput Info Tech (ECTI-CIT) 8(1):67–79
Shrestha N, Nasoz F (2019) Deep learning sentiment analysis of amazon.com reviews and ratings. Int J Soft Comp, Artif Intell Appl 8(1):1–15
Chauhan U, Afzal M, Shahid A, Abdar M, Basiri M, Zhou X-J (2020) A comprehensive analysis of adverb types for mining user sentiments on amazon product reviews. World Wide Web 23(3):1811–1829
Shenoy A, Sardana A (2020) Multilogue-net: A context aware rnn for multi-modal emotion detection and sentiment analysis in conversation. arXiv preprint arXiv:2002.08267
Goel S, Banthia M, Sinha A (2018) Modeling recommendation system for real time analysis of social media dynamics. In: 2018 Eleventh International Conference on Contemporary Computing (IC3). IEEE, pp 1–5
(2022) A systematic review on Tanpin Kandri based crime prediction. Vijay, K., Sowmia, K. R., Jananee, V., Remit Rev 7(2): 1–11
Wu Y, Ren F (2011) Learning sentimental influence in twitter. In: 2011 International Conference on Future Computer Sciences and Application. IEEE, pp 119–122
Sowmia KR, Poonkuzhali S, Jeyalakshmi J (2023) Sentiment classification of higher education reviews to analyze students’ engagement and psychology interventions using deep learning techniques. In: Smart Trends in Computing and Communications. Springer, Singapore, pp 257–265
Vijay P, Ojha S, Sriram V, Nanthiniannal J (2020) Block chain in supply chain tracability. Int J Comput Sci Mobile Comput 9(3):96–99 zain Publication
Prithi S, Sumathi S (2016) Review on Grouping Algorithms for Finite State Automata
Rajeswari P, Jayashree K (2018) Survey on QoS metrics and ranking in cloud services. Int J Eng Technol 7:146–149
Arockia Raj Y, Alli P (2012) An analysis and overview of modern digital watermarking. Am J Appl Sci 9(1):66–70
Kaur C, Sharma A (2020) Social issues sentiment analysis using python. In: 2020 5th international conference on computing, communication and security (ICCCS). IEEE, pp 1–6
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical approval and human participation
No ethics approval is required.
Consent to participate
Not Applicable
Consent for publication
Not Applicable
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
K, V., Samuel, P., Krishna, B.V. et al. Exploration of sentiment analysis in twitter propaganda: a deep dive. Multimed Tools Appl 83, 44729–44751 (2024). https://doi.org/10.1007/s11042-023-17383-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-17383-6