Emotion recognition in election day tweets using optimised kernel extreme learning machine classifier

References

• Alessia, D., Ferri, F., Grifoni, P., & Guzzo, T. (2015). Approaches, tools and applications for sentiment analysis implementation. International Journal of Computer Applications, 125(3). https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.695.2027&rep=rep1&type=pdf
   Google Scholar
• Baliarsingh, S. K., Vipsita, S., Muhammad, K., Dash, B., & Bakshi, S. (2019, April 1). Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm. Applied Soft Computing, 77, 520–532. https://doi.org/10.1016/j.asoc.2019.01.007
   Web of Science ®Google Scholar
• Chowdhury, S. M., Ghosh, P., Abujar, S., Afrin, M. A., & Hossain, S. A. (2019). Sentiment Analysis of tweet data: The study of sentimental state of human from tweet text. In Abraham, A. (Eds.), Emerging technologies in data mining and information security (pp. 3–14). Springer.
   Google Scholar
• Dvoynikova, A., Verkholyak, O., & Karpov, A. (2020). Emotion recognition and sentiment analysis of extemporaneous speech transcriptions in Russian. In International Conference on Speech and Computer, Springer, Cham, 136–144.
   Google Scholar
• Elghazaly, T., Mahmoud, A., & Hefny, H. A. (2016 March 22). Political sentiment analysis using twitter data. In Proceedings of the International Conference on Internet of things and Cloud Computing, ACM, Cambridge, United Kingdom, 11.
   Google Scholar
• Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., & Heidari, A. A. (2019). Salp Swarm algorithm: Theory, literature review, and application in extreme learning machines. In Seyedali Mirjalili (Eds.), Nature-Inspired Optimizers (pp. 185–199). Springer.
   Google Scholar
• Gaikar, D., & Marakarkandy, B. (2015). Product sales prediction based on sentiment analysis using twitter data. International Journal of Computer Science and Information Technologies, 6(3), 2303–2313. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.735.2632&rep=rep1&type=pdf
   Google Scholar
• Hassan, M. K., Shakthi, S. P., & Sasikala, R. (2017, November). Sentimental analysis of Amazon reviews using naïve bayes on laptop products with MongoDB and R. IOP Conference Series: Materials Science and Engineering, 263(4), 042090. https://doi.org/10.1088/1757-899X/263/4/042090
   Google Scholar
• He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015). A novel social media competitive analytics framework with sentiment benchmarks. Information & Management, 52(7), 801–812. https://doi.org/10.1016/j.im.2015.04.006
   Web of Science ®Google Scholar
• Jagdale, R. S., Shirsat, V. S., & Deshmukh, S. N. (2019). Sentiment analysis on product reviews using machine learning techniques. In Pradeep Kumar Mallick (Eds.), Cognitive informatics and soft computing (pp. 639–647). Springer.
   Google Scholar
• Jain, V. K., Kumar, S., & Fernandes, S. L. (2017). Extraction of emotions from multilingual text using intelligent text processing and computational linguistics. Journal of Computational Science, 21, 316–326. https://doi.org/10.1016/j.jocs.2017.01.010
   Web of Science ®Google Scholar
• Kern, M. L., Park, G., Eichstaedt, J. C., Schwartz, H. A., Sap, M., Smith, L. K., & Ungar, L. H. (2016, December). Gaining insights from social media language: Methodologies and challenges. Psychological Methods, 21(4), 507. https://doi.org/10.1037/met0000091
   PubMed Web of Science ®Google Scholar
• Li, Q., Chen, H., Huang, H., Zhao, X., Cai, Z., Tong, C., Liu, W., & Tian, X. (2017). An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Computational and Mathematical Methods in Medicine, 2017, 1–15. https://doi.org/10.1155/2017/9512741
   Google Scholar
• Lu, H., Du, B., Liu, J., Xia, H., & Yeap, W. K. (2017). A kernel extreme learning machine algorithm based on improved particle swam optimization. Memetic Computing, 9(2), 121–128. https://doi.org/10.1007/s12293-016-0182-5
   Web of Science ®Google Scholar
• Martins, M. S. G. D. C. (2017). How TripAdvisor’s reviewers level of expertise influence their online rating behaviour and the usefulness of reviews. PhD diss.
   Google Scholar
• Pagolu, V. S., Reddy, K. N., Panda, G., & Majhi, B. (2016). Sentiment analysis of Twitter data for predicting stock market movements. In 2016 international conference on signal processing, communication, power and embedded system (SCOPES), IEEE, Paralakhemundi, India, 1345–1350.
   Google Scholar
• Qazi, A., Tamjidyamcholo, A., Raj, R. G., Hardaker, G., & Standing, C. (2017, Oct 1). Assessing consumers‘ satisfaction and expectations through online opinions: Expectation and disconfirmation approach. Computers in Human Behavior, 75, 450–460. https://doi.org/10.1016/j.chb.2017.05.025
   Web of Science ®Google Scholar
• Rout, J. K., Choo, K. K., Dash, A. K., Bakshi, S., Jena, S. K., & Williams, K. L. (2018, March). A model for sentiment and emotion analysis of unstructured social media text. Electronic Commerce Research, 18(1), 181–199. https://doi.org/10.1007/s10660-017-9257-8
   Web of Science ®Google Scholar
• Shah, F. M., Reyadh, A. S., Shaafi, A. I., Ahmed, S., & Sithil, F. T. (2019). Emotion detection from tweets using AIT-2018 Dataset. In 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, Dhaka, Bangladesh, 575–580.
   Google Scholar
• Shivaprasad, T. K., & Shetty, J. (2017 March 10). Sentiment analysis of product reviews: A review. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), IEEE, Coimbatore, Tamilnadu, India, 298–301.
   Google Scholar
• Suhasini, M., & Srinivasu, B. (2020). Emotion detection framework for twitter data using supervised classifiers. In Raju, K. S. (Eds.), Data Engineering and Communication Technology (pp. 565–576). Springer.
   Google Scholar
• Toçoğlu, M. A., & Alpkocak, A. (2019). Lexicon-based emotion analysis in Turkish. Turkish Journal of Electrical Engineering & Computer Sciences, 27(2), 1213–1227. https://doi.org/10.3906/elk-1807-41
   Web of Science ®Google Scholar
• Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117–126. https://doi.org/10.1016/j.eswa.2016.03.028
   Web of Science ®Google Scholar
• Wycech, S. (2015). An investigation of attitudes towards mobile payments. PhD diss., University of Dublin.
   Google Scholar