A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning
DOI:
https://doi.org/10.25007/ajnu.v11n1a1250Keywords:
Emotion Analysis, Social Network, Ensemble Learning, Deep learningAbstract
Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.
Downloads
References
R. K. Behera, M. Jena, S. K. Rath, and S. Misra, "Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data," Information Processing & Management, vol. 58, no. 1, p. 102435, 2021.
D. A. Pereira, "A survey of sentiment analysis in the Portuguese language," Artificial Intelligence Review, vol. 54, no. 2, pp. 1087-1115, 2021.
K. Sailunaz and R. Alhajj, "Emotion and sentiment analysis from Twitter text," Journal of Computational Science, vol. 36, p. 101003, 2019.
A. Zadeh, M. Chen, S. Poria, E. Cambria, and L.-P. Morency, "Tensor fusion network for multimodal sentiment analysis," arXiv preprint arXiv:1707.07250, 2017.
J. K. Rout, K.-K. R. Choo, A. K. Dash, S. Bakshi, S. K. Jena, and K. L. Williams, "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, vol. 18, no. 1, pp. 181-199, 2018.
C. Dos Santos and M. Gatti, "Deep convolutional neural networks for sentiment analysis of short texts," in Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2014, pp. 69-78.
A. Serna, A. Soroa, and R. Agerri, "Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport," Sustainability, vol. 13, no. 4, p. 2397, 2021.
A. Dhillon and G. K. Verma, "Convolutional neural network: a review of models, methodologies and applications to object detection," Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85-112, 2020.
N. Aloysius and M. Geetha, "A review on deep convolutional neural networks," in 2017 International Conference on Communication and Signal Processing (ICCSP), 2017, pp. 0588-0592: IEEE.
G. Yao, T. Lei, and J. Zhong, "A review of convolutional-neural-network-based action recognition," Pattern Recognition Letters, vol. 118, pp. 14-22, 2019.
S. Sony, K. Dunphy, A. Sadhu, and M. Capretz, "A systematic review of convolutional neural network-based structural condition assessment techniques," Engineering Structures, vol. 226, p. 111347, 2021.
!!! INVALID CITATION !!! {}.
N. F. Waziralilah, A. Abu, M. Lim, L. K. Quen, and A. Elfakharany, "A review on convolutional neural network in bearing fault diagnosis," in MATEC Web of Conferences, 2019, vol. 255, p. 06002: EDP Sciences.
J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, "A comprehensive survey on support vector machine classification: Applications, challenges and trends," Neurocomputing, vol. 408, pp. 189-215, 2020.
X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, "A survey on ensemble learning," Frontiers of Computer Science, vol. 14, no. 2, pp. 241-258, 2020.
X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, "An adaptive ensemble machine learning model for intrusion detection," IEEE Access, vol. 7, pp. 82512-82521, 2019.
C. Zhang and Y. Ma, Ensemble machine learning: methods and applications. Springer, 2012.
H. M. Gomes, J. P. Barddal, F. Enembreck, and A. Bifet, "A survey on ensemble learning for data stream classification," ACM Computing Surveys (CSUR), vol. 50, no. 2, p. 23, 2017.
J. Serrano-Guerrero, J. A. Olivas, F. P. Romero, and E. Herrera-Viedma, "Sentiment analysis: A review and comparative analysis of web services," Information Sciences, vol. 311, pp. 18-38, 2015.
M. Z. Asghar, A. Khan, S. Ahmad, and F. M. Kundi, "A review of feature extraction in sentiment analysis," Journal of Basic and Applied Scientific Research, vol. 4, no. 3, pp. 181-186, 2014.
H. Peng, E. Cambria, and A. Hussain, "A review of sentiment analysis research in Chinese language," Cognitive Computation, vol. 9, no. 4, pp. 423-435, 2017.
A. Krouska, C. Troussas, and M. Virvou, "Deep learning for twitter sentiment analysis: the effect of pre-trained word embedding," in Machine Learning Paradigms: Springer, 2020, pp. 111-124.
M. Umer, I. Ashraf, A. Mehmood, S. Kumari, S. Ullah, and G. Sang Choi, "Sentiment analysis of tweets using a unified convolutional neural network‐long short‐term memory network model," Computational Intelligence, vol. 37, no. 1, pp. 409-434, 2021.
H. Juwiantho, E. I. Setiawan, J. Santoso, and M. H. Purnomo, "Sentiment Analysis Twitter Bahasa Indonesia Berbasis Word2Vec Menggunakan Deep Convolutional Neural Network," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 1, pp. 181-188, 2020.
W. L. Lim, C. C. Ho, and C.-Y. Ting, "Tweet sentiment analysis using deep learning with nearby locations as features," in Computational Science and Technology: Springer, 2020, pp. 291-299.
I. Kandasamy, W. Vasantha, J. M. Obbineni, and F. Smarandache, "Sentiment analysis of tweets using refined neutrosophic sets," Computers in Industry, vol. 115, p. 103180, 2020.
R. P. Mehta, M. A. Sanghvi, D. K. Shah, and A. Singh, "Sentiment analysis of tweets using supervised learning algorithms," in First International Conference on Sustainable Technologies for Computational Intelligence, 2020, pp. 323-338: Springer.
N. Chouchani and M. Abed, "Enhance sentiment analysis on social networks with social influence analytics," Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 1, pp. 139-149, 2020.
A. H. Ombabi, W. Ouarda, and A. M. Alimi, "Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks," Social Network Analysis and Mining, vol. 10, no. 1, pp. 1-13, 2020.
K. Pasupa and T. S. N. Ayutthaya, "Hybrid deep learning models for thai sentiment analysis," Cognitive Computation, pp. 1-27, 2021.
A. Hassan and A. Mahmood, "Deep learning approach for sentiment analysis of short texts," in 2017 3rd international conference on control, automation and robotics (ICCAR), 2017, pp. 705-710: IEEE.
A. Agrawal and A. An, "Kea: Sentiment analysis of phrases within short texts," in Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 380-384.
C. Bouras and V. Tsogkas, "Noun retrieval effect on text summarization and delivery of personalized news articles to the user’s desktop," Data & Knowledge Engineering, vol. 69, no. 7, pp. 664-677, 2010.
A. K. Dipongkor, M. A. Nashiry, K. A. Abdullah, and R. S. Ritu, "A Study on Bengali Stemming and Parts-of-Speech Tagging," in Emerging Technologies in Data Mining and Information Security: Springer, 2021, pp. 35-44.
Z. Nanli, Z. Ping, L. Weiguo, and C. Meng, "Sentiment analysis: A literature review," in 2012 International Symposium on Management of Technology (ISMOT), 2012, pp. 572-576: IEEE.
I. K. Raharjana, D. Siahaan, and C. Fatichah, "User Stories and Natural Language Processing: A Systematic Literature Review," IEEE Access, vol. 9, pp. 53811-53826, 2021.
K. K. Chandriah and R. V. Naraganahalli, "RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting," Multimedia Tools and Applications, pp. 1-15, 2021.
L. Lakshmi, M. P. Reddy, C. Santhaiah, and U. J. Reddy, "Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM," Wireless Personal Communications, vol. 118, no. 4, pp. 3549-3564, 2021.
M. Yaqub et al., "State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images," Brain Sciences, vol. 10, no. 7, p. 427, 2020.
D. O. Melinte and L. Vladareanu, "Facial expressions recognition for human–robot interaction using deep convolutional neural networks with rectified adam optimizer," Sensors, vol. 20, no. 8, p. 2393, 2020.
O. Araque, I. Corcuera-Platas, J. F. Sanchez-Rada, and C. A. Iglesias, "Enhancing deep learning sentiment analysis with ensemble techniques in social applications," Expert Systems with Applications, vol. 77, pp. 236-246, 2017.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Umran Abdullah Haje, Mohammed Hussein Abdalla, Reben Mohammed Saleem Kurda, Zhwan Mohammed Khalid
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors retain copyright
The use of a Creative Commons License enables authors/editors to retain copyright to their work. Publications can be reused and redistributed as long as the original author is correctly attributed.
- Copyright
- The researcher(s), whether a single or joint research paper, must sell and transfer to the publisher (the Academic Journal of Nawroz University) through all the duration of the publication which starts from the date of entering this Agreement into force, the exclusive rights of the research paper/article. These rights include the translation, reuse of papers/articles, transmit or distribute, or use the material or parts(s) contained therein to be published in scientific, academic, technical, professional journals or any other periodicals including any other works derived from them, all over the world, in English and Arabic, whether in print or in electronic edition of such journals and periodicals in all types of media or formats now or that may exist in the future. Rights also include giving license (or granting permission) to a third party to use the materials and any other works derived from them and publish them in such journals and periodicals all over the world. Transfer right under this Agreement includes the right to modify such materials to be used with computer systems and software, or to reproduce or publish it in e-formats and also to incorporate them into retrieval systems.
- Reproduction, reference, transmission, distribution or any other use of the content, or any parts of the subjects included in that content in any manner permitted by this Agreement, must be accompanied by mentioning the source which is (the Academic Journal of Nawroz University) and the publisher in addition to the title of the article, the name of the author (or co-authors), journal’s name, volume or issue, publisher's copyright, and publication year.
- The Academic Journal of Nawroz University reserves all rights to publish research papers/articles issued under a “Creative Commons License (CC BY-NC-ND 4.0) which permits unrestricted use, distribution, and reproduction of the paper/article by any means, provided that the original work is correctly cited.
- Reservation of Rights
The researcher(s) preserves all intellectual property rights (except for the one transferred to the publisher under this Agreement).
- Researcher’s guarantee
The researcher(s) hereby guarantees that the content of the paper/article is original. It has been submitted only to the Academic Journal of Nawroz University and has not been previously published by any other party.
In the event that the paper/article is written jointly with other researchers, the researcher guarantees that he/she has informed the other co-authors about the terms of this agreement, as well as obtaining their signature or written permission to sign on their behalf.
The author further guarantees:
- The research paper/article does not contain any defamatory statements or illegal comments.
- The research paper/article does not violate other's rights (including but not limited to copyright, patent, and trademark rights).
This research paper/article does not contain any facts or instructions that could cause damages or harm to others, and publishing it does not lead to disclosure of any confidential information.
