Abstract
Twitter has developed into a significant social media network and is a topic of great interest for sentiment analysis researchers. Twitter is a crucial information source for learning about people's attitudes, feelings, viewpoints, and feedback. Every day, millions of reviews are written with both good and negative or indifferent feedback. The procedure of analyzing this generated review is difficult and time-consuming. This research utilized a beluga dodger optimization-based ensemble classifier to recognize and categorizes the sentiments in social media in order to overcome this problem. The ensemble classifier is combined with the beluga dodger (BD) optimization to create the classifier. Convolutional neural networks and bidirectional long short-term memory classifiers were combined to create the hybrid ensemble classifier, which performs more efficiently. In order to improve classification performance and obtain sentiment prediction more quickly, the proposed beluga dodger optimization started by mixing shark hunting characteristics with whale optimization. In comparison with all other approaches, the BD-optimized deep ensemble classifier achieved values of 97.61% accuracy, 96.20% sensitivity, and 98.61% specificity.
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References
Abedinia O, Amjady N, Ghasemo A (2016) A new metaheuristic algorithm based on shark smell optimization. Complexity 21(5):97–116
Alattar F, Shaalan K (2021) Using artificial intelligence to understand what causes sentiment changes on social media. IEEE Access 9:61756–61767
Alotaibi B, Abbasi RA, Aslam MA, Saeedi K, Alahmadi D (2020) Startup initiative response analysis (SIRA) framework for analyzing startup initiatives on twitter. IEEE Access 8:10718–10730
Alsayat A (2022) Improving sentiment analysis for social media applications using an ensemble deep learning language model. Arab J Sci Eng 47(2):2499–2511
Al-Twairesh N, Al-Negheimish H (2019) Surface and deep features ensemble for sentiment analysis of arabic tweets. IEEE Access 7:84122–84131
Araque O, Corcuera-Platas I, Sánchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77(19):236–246
Asghar MZ, Khan A, Ahmad S, Qasim M, Khan IA (2017) Lexiconenhanced sentiment analysis framework using rule-based classification scheme. PLoS ONE 12(2). Art. no. e0171649
Babu NV, Kanaga EGM (2022) Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput Sci 3:1–20
Bianchi F, Nozza D, Hovy D (2022) XLM-EMO: Multilingual emotion prediction in social media text. I:n Proceedings of the 12th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp 195–203
Bibi M, Aziz W, Almaraashi M, Khan IH, Nadeem MSA, Habib N (2020) A cooperative binary-clustering framework based on majority voting for twitter sentiment analysis. IEEE Access 8:68580–68592
Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8
Boukabous M, Azizi M (2022) Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers. Indones J Electr Eng Comput Sci 25(2):1131–1139
Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107
Chandrasekaran G, Antoanela N, Andrei G, Monica C, Hemanth J (2022) Visual sentiment analysis using deep learning models with social media data. Appl Sci 12(3):1030
Chen H, Xu Y, Wang M, Zhao X (2019) A balanced whale optimization algorithm for constrained engineering design problems. Appl Math Model 71:45–59
D’Avanzo E, Pilato G (2015) Mining social network users opinions’ to aid buyers’ shopping decisions. Comput Hum Behav 51:1284–1294
Dangi D, Dixit DK, Bhagat A (2022) Sentiment analysis of COVID-19 social media data through machine learning. Multimedia Tools Appl 81(29):42261–42283
Das S, Kalita HK (2017) Sentiment analysis for Web-based big data: a survey. Int J Adv Res Comput Sci 8(5):1996–1999
Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1(12)
Hajarian M, Bastanfard A, Mohammadzadeh J, Khalilian M (2019) SNEFL: social network explicit fuzzy like dataset and its application for Incel detection. Multimedia Tools Appl 78:33457–33486
Hu T, She B, Duan L, Yue H, Clunis J (2020) A Systematic spatial and temporal sentiment analysis on geo-tweets. IEEE Access 8:8658–8667
Ji X, Chun SA, Wei Z, Geller J (2015) Twitter sentiment classification for measuring public health concerns. Social Netw Anal Mining 5(1):13
Jungherr A (2016) Twitter use in election campaigns: a systematic literature review. J Inf Technol Politics 13(1):72–91
Kokab ST, Asghar S, Naz S (2022) Transformer-based deep learning models for the sentiment analysis of social media data. Array 14:100157
Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167
Lu Q, Zhu Z, Zhang D, Wu W, Guo Q (2020) Interactive rule attention network for aspect-level sentiment analysis. IEEE Access 8:52505–52516
Mameli M, Paolanti M, Morbidoni C, Frontoni E, Teti A (2022) Social media analytics system for action inspection on social networks. Soc Netw Anal Min 12(1):33
Martinez-Camara E, Martin-Valdivia MT, Urena-Lopez LA, Montejo-Raez AR (2014) Sentiment analysis in Twitter. Natural Lang Eng 20(1):1–28
Menkhoff T, Chay YW, Bengtsson ML, Woodard CJ, Gan B (2015) Incorporating microblogging (‘tweeting’) in higher education: lessons learnt in a knowledge management course. Comput Hum Behav 51:1295–1302
Naseem U, Razzak I, Khushi M, Eklund PW, Kim J (2021) COVIDSenti: a large-scale benchmark twitter data set for COVID-19 sentiment analysis. IEEE Trans Comput Soc Syst 8(4):1003–1015
Omuya EO, Okeyo G, Kimwele M (2023) Sentiment analysis on social media tweets using dimensionality reduction and natural language processing. Eng Rep 5(3):e12579
Phan HT, Tran VC, Nguyen NT, Hwang D (2020) Improving the performance of sentiment analysis of tweets containing fuzzy sentiment using the feature ensemble model. IEEE Access 8:14630–14641
Pressman SD, Gallagher MW, Lopez SJ (2013) ‘Is the emotionhealth connection a ‘First-world problem?’ Psychol Sci 24(4):544–549
Ruz GA, Henríquez PA, Mascareño A (2020) Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Futur Gener Comput Syst 106:92–104
Savargiv M, Bastanfard A (2013) Text material design for fuzzy emotional speech corpus based on persian semantic and structure. In: 2013 International conference on fuzzy theory and its applications (iFUZZY) (pp. 380–384). IEEE
Shelke N, Chaudhury S, Chakrabarti S, Bangare SL, Yogapriya G, Pandey P (2022) An efficient way of text-based emotion analysis from social media using LRA-DNN. Neurosci Inf, p. 100048
Tighe P, Goldsmith R, Gravenstein M, Bernard H, Fillingim R (2015) The painful tweet: text, sentiment, and community structure analyses of tweets pertaining to pain. J Med Internet Res 17(4):1–19
“Twitter sentiment analysis” from https://www.kaggle.com/c/twitter-sentiment-analysis2
Waheeb SA, Khan NA, Shang X (2022) Topic modeling and sentiment analysis of online education in the COVID-19 era using social networks based datasets. Electronics 11(5):715
Wang Y (2018) Sensing human sentiment via social media images: Methodologies and applications. Ph.D. dissertation, Dept. Comput. Sci. Eng., Arizona State Univ., Phoenix, AZ, USA
Wang L, Niu J, Yu S (2020) SentiDiff: combining textual information and sentiment diffusion patterns for twitter sentiment analysis. IEEE Trans Knowl Data Eng 32(10):2026–2039
Xia Y, Cambria E, Hussain A, Zhao H (2015) Word polarity disambiguation using Bayesian model and opinion-level features. Cognit Comput 7(3):369–380
Yu Y, Wang X (2015) World cup 2014 in the Twitter world: a big data analysis of sentiments in U.S. Sports fans’ tweets. Comput Hum Behav 48:392–400
Zhou J, Jin S, Huang X (2020) ADeCNN: an improved model for aspect-level sentiment analysis based on deformable CNN and attention. IEEE Access 8:132970–132979
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Vinod, P., Sheeja, S. Sentiment prediction model in social media data using beluga dodger optimization-based ensemble classifier. Soc. Netw. Anal. Min. 13, 107 (2023). https://doi.org/10.1007/s13278-023-01111-x
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DOI: https://doi.org/10.1007/s13278-023-01111-x