Optimization of Feature Set for Sentiment Analysis using Artificial Butterfly Algorithm and Ensemble Machine Learning
Keywords:Deep neural networks, Aspect based sentiment analysis, Accuracy.
The current decade has witnessed the remarkable developments in the field of artificial intelligence, and the revolution of deep learning has transformed the whole artificial intelligence industry. Eventually, deep learning techniques have become essential components of any model in today’s computational world. Nevertheless, ensemble learning techniques promise a high degree of automation with generalized rule extraction for both text and sentiment classification tasks. This paper aims designed and implemented optimized feature matrix using ensemble learning used for sentiment classification and its applications.
L. Yang, Y. Li, J. Wang and R. S. Sherratt, "Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning," in IEEE Access, vol. 8, pp. 23522-23530, 2020.
G. Xu, Z. Yu, Z. Chen, X. Qiu and H. Yao, "Sensitive Information Topics-Based Sentiment Analysis Method for Big Data," in IEEE Access, vol. 7, pp. 96177-96190, 2019.
V. Ramanathan and T. Meyyappan, "Twitter Text Mining for Sentiment Analysis on People’s Feedback about Oman Tourism," 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), Muscat, Oman, 2019, pp. 1-5.
G. Xu, Y. Meng, X. Qiu, Z. Yu and X. Wu, "Sentiment Analysis of Comment Texts Based on BiLSTM," in IEEE Access, vol. 7, pp. 51522-51532, 2019.
F. Iqbal et al., "A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction," in IEEE Access, vol. 7, pp. 14637-14652, 2019.
M. Wongkar and A. Angdresey, "Sentiment Analysis Using Naive Bayes Algorithm of The Data Crawler: Twitter," 2019 Fourth International Conference on Informatics and Computing (ICIC), Semarang, Indonesia, 2019, pp. 1-5.
X. Hu, J. Tang, H. Gao, and H. Liu, ‘‘Unsupervised sentiment analysis with emotional signals,’’ in Proc. 22nd Int. Conf. World Wide Web, 2013, pp. 607–618.
A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, ‘‘Sentiment analysis of Twitter data,’’ in Proc. Workshop Lang. Social Media, 2011, pp. 30–38.
M. Pontiki et al., ‘‘SemEval-2016 task 5: Aspect based sentiment analysis,’’ in Proc. 8th Int. Workshop Semantic Eval. (SemEval), 2014, pp. 27–35.
P. C. S. Njølstad, L. S. Høysæter, W. Wei, and J. A. Gulla, ‘‘Evaluating feature sets and classifiers for sentiment analysis of financial news,’’ in Proc. IEEE/WIC/ACM Int. Joint Conf. Web Intell. (WI) Intell. Agent Technol. (IAT), vol. 2, Aug. 2014, pp. 71–78
S. Shikhar, et al. “LEXER: LEXicon Based Emotion AnalyzeR.” International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham, 2017.
Low, Lu-Shih Alex, et al. “Content based clinical depression detection in adolescents.” Signal Processing Conference, 2009 17th European. IEEE, 2009.
Wang, Xinyu, et al. “A depression detection model based on sentiment analysis in micro-blog social network.” Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2013.
Wang, Xinyu, Chunhong Zhang, and Li Sun. “An improved model for depression detection in micro-blog social network.” 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013.
Shen, Tiancheng, “Cross Domain Depression Detection via Harvesting Social Media.” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. 2018.
How to Cite
Copyright (c) 2020 Jyoti Hanvat, Sumit Sharma
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.