Abstract
Sentiment analysis refers to the interpretation and computational study of emotions, opinions and appraisals within the text data using text analysis methods. A basic aim of sentiment analysis is to categorize the sentiment polarity of the sentences, document or aspects. Product manufacturers use the knowledge from sentiment analysis for improving their services & products. Hence, there is an atrocious need of an efficient technique that can accurately identify the sentiment polarity of the content. The supervised classification algorithm has been proved favourable for most of the sentiment analysis task and is widely used in opinion mining. This study presents a novel method for sentiment analysis by combining two supervised classification algorithms viz. Decision Tree (DT) and Feed Forward Neural Network (FNN). Pre-processing of data is carried out by using Independent Component Analysis (ICA) and Windowed Multivariate Autoregressive Model (WMAR) is introduced for extraction of potential features. Then highest scores are extracted using Improved Bat Algorithm (IBA) technique and finally, the experimental results are compared with existing algorithms i.e. ID3, J48 and Random forest classifier. The proposed method significantly outperforms the existing sentiment classification methods with accuracy of 97.84%.
Similar content being viewed by others
References
Abdullah M, AlMasawa M, Makki I, Alsolmi M, Mahrous S (2018) Emotions extraction from Arabic tweets. Int J Comput Appl 1–15
Agarwal A et al. (2011) Sentiment analysis of twitter data. Proceedings of the workshop on languages in social media. Association for Computational Linguistics
Ahuja R, Chug A, Kohli S, Gupta S, Ahuja P (2019) The impact of features extraction on the sentiment analysis. Procedia Computer Science 152:341–348
Alharbi ASM, de Doncker E (2019) Twitter sentiment analysis with a deep neural network: an enhanced approach using user behavioral information. Cogn Syst Res 54:50–61
Alsmadi I, Hoon GK (2019) Term weighting scheme for short-text classification: Twitter corpuses. Neural Computing and Applications 31(8):3819–3831
Avinash M, Sivasankar E (2019) A study of feature extraction techniques for sentiment analysis. In Emerging Technologies in Data Mining and Information Security (pp. 475-486). Springer, Singapore.
Bahrainian S-A, Dengel A (2013) Sentiment analysis and summarization of twitter data. Computational Science and Engineering (CSE), 2013 IEEE 16th International Conference on. IEEE
Beskirli M, Koc I (2015) A comparative study of improved bat algorithm and bat algorithm on numerical benchmarks. In 2015 4th International conference on advanced computer science applications and technologies (ACSAT) (pp. 68-73). IEEE
Bhadane C, Dalal H, Doshi H (2015) Sentiment analysis: measuring opinions, Science Direct, pp 808–815.
Das S, Behera RK, Rath SK (2018) Real-time sentiment analysis of twitter streaming data for stock prediction. Procedia computer science 132:956–964
Dridi A, Recupero DR (2019) Leveraging semantics for sentiment polarity detection in social media. International Journal of Machine Learning and Cybernetics 10(8):2045–2055
El Alaoui I, Gahi Y, Messoussi R, Chaabi Y, Todoskoff A, Kobi A (2018) A novel adaptable approach for sentiment analysis on big social data. Journal of Big Data 5(1):12
Gautam G, Yadav D (2014) Sentiment analysis of twitter data using machine learning approaches and semantic analysis. Contemporary computing (IC3), 2014 seventh international conference on. IEEE
Greco F, Polli A (2019) Emotional text mining: customer profiling in brand management. Int J Inf Manag 101934
Guo SS, Wang JS, Ma XX (2019) Improved bat algorithm based on multipopulation strategy of island model for solving global function optimization problem Computational Intelligence and Neuroscience, 2019.
Gurkhe D, Bhatia R (2014) Effective sentiment analysis of social media datasets using naive Bayesian classification
Huang GB, Wang DH, Lan Y (2011) Extreme Learning Machines: A Survey. International Journal of Machine Learning and Cybernetics 2(2):107–122
Karlsson B, Hassan M, Marque C (2013) Windowed multivariate autoregressive model improving classification of labor vs. pregnancy contractions. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 7444-7447). IEEE.
Kumar A, Jaiswal A (2020) Systematic literature review of sentiment analysis on twitter using soft computing techniques. Concurrency and Computation: Practice and Experience 32(1):e5107
Kumar HM, Harish BS, Darshan HK (2019) Sentiment analysis on IMDb movie reviews using hybrid feature extraction method. International Journal of Interactive Multimedia & Artificial Intelligence, 5(5)
Liao C, Feng C, Yang S, Huang H (2016) Topic-related Chinese message sentiment analysis. Neurocomputing 210:237–246
Mäntylä MV, Graziotin D, Kuutila M (2018) The evolution of sentiment analysis—a review of research topics, venues, and top cited papers. Computer Science Review 27:16–32
Neethu MS, Rajasree R (2013) Sentiment analysis in twitter using machine learning techniques. Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on. IEEE
O'dea B, Wan S, Batterham PJ, Calear AL, Paris C, Christensen H (2015) Detecting suicidality on twitter. Internet Interv 2(2):183–188
Ortigosa A, Martín JM, Carro RM (2014) Sentiment analysis in Facebook and its application to e-learning. Comput Hum Behav 31:527–541
Osaba E, Yang XS, Fister I Jr, Del Ser J, Lopez-Garcia P, Vazquez-Pardavila AJ (2019) A discrete and improved bat algorithm for solving a medical goods distribution problem with pharmacological waste collection. Swarm and evolutionary computation 44:273–286
Rosenthal S, Mohammad SM, Nakov P, Ritter A, Kiritchenko S, Stoyanov V (2019) Semeval-2015 task 10: Sentiment analysis in twitter. arXiv preprint arXiv:1912.02387
Stojanovski D, Strezoski G, Madjarov G, Dimitrovski I, Chorbev I (2018) Deep neural network architecture for sentiment analysis and emotion identification of twitter messages. Multimed Tools Appl 77(24):32213–32242
Suhasini M, Srinivasu B (2020) Emotion Detection Framework for Twitter Data Using Supervised Classifiers. In Data Engineering and Communication Technology, pp. 565–576. Springer, Singapore
Symeonidis S, Effrosynidis D, Arampatzis A (2018) A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Syst Appl 110:298–310
Troussas, C., Krouska, A., &Virvou, M. (2016, July). Evaluation of ensemble-based sentiment classifiers for twitter data. In 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-6). IEEE.
Troussas C, Krouska A, Virvou M (2016) Evaluation of ensemble-based sentiment classifiers for Twitter data. In 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–6. IEEE
Yilmaz S, Kucuksille EU (2013) Improved bat algorithm (IBA) on continuous optimization problems. Lecture Notes on Software Engineering 1(3):279
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Naz, H., Ahuja, S., Kumar, D. et al. DT-FNN based effective hybrid classification scheme for twitter sentiment analysis. Multimed Tools Appl 80, 11443–11458 (2021). https://doi.org/10.1007/s11042-020-10190-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10190-3