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کلاسبندی پیامهای منتشر شده در شبکههای اجتماعی در بحران کرونا براساس قطبیت آنها | ||
| مدل سازی در مهندسی | ||
| دوره 21، شماره 74، آبان 1402، صفحه 81-93 اصل مقاله (840.34 K) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2023.29412.2384 | ||
| نویسندگان | ||
| هانیه تندرست1؛ سمیرا عباسی* 1؛ فاطمه امیری2 | ||
| 1گروه مهندسی پزشکی، دانشگاه صنعتی همدان، همدان، ایران. | ||
| 2گروه مهندسی کامپیوتر، دانشگاه صنعتی همدان، همدان، ایران. | ||
| تاریخ دریافت: 09 دی 1401، تاریخ بازنگری: 19 فروردین 1402، تاریخ پذیرش: 06 اردیبهشت 1402 | ||
| چکیده | ||
| بحران کرونا مردم ایران را با طیف گستردهای از افکار و احساسات مثبت و منفی روبرو کرد. مردم این احساسات را در شبکههای اجتماعی به اشتراک میگذاشتند. شبکههای اجتماعی در دوران کرونا نقش بسیار مهمی در انتشار اطلاعات و بازتاب احساسات مردم داشتهاند. بررسی این دادههای شبکههای اجتماعی برای دولتها و سازمانهای بهداشت در سراسر جهان حائز اهمیت است. به همین خاطر پژوهشهای زیادی به بررسی این دادهها با رویکردهای مختلف در سراسر جهان پرداختند. در این مقاله نیز به تحلیل قطبیت و کلاسبندی پیامهای منتشر شده در شبکههای اجتماعی در بحران کرونا پرداخته شد. برای این منظور پیامهایی که کاربران فارسی زبان در این شبکهها به اشتراک گذاشتند، بررسی شدند. برای کلاسبندی دادههای موجود از روشهای پردازش زبان طبیعی و روشهای یادگیری عمیق استفاده شد. برای کلاسبندی پیامها با محتوای مثبت و منفی، روشهای یادگیری عمیق مختلفی با معماری های متفاوت (شامل شبکه های کانولوشنی، شبکه های بازگشتی عمیق با حافظه و فازی-بازگشتی عمیق با حافظه) با توجه به دادههای موجود اعمال شد تا بتوانیم به بهترین نتیجه ممکن دست یابیم. بهترین نتیجه با استفاده از شبکههای عمیق کانولوشنی سهلایه به دست آمد که صحت آن 72.29 بود. در نهایت، یک مقایسه کلی از جنبههای مختلف، روی شبکههای استفاده شده انجام شد. | ||
| کلیدواژهها | ||
| شبکههای اجتماعی؛ ویروس کرونا؛ پردازش زبان طبیعی؛ یادگیری عمیق؛ قطبیت | ||
| عنوان مقاله [English] | ||
| Classification of social media posts during the Corona crisis based on their polarity | ||
| نویسندگان [English] | ||
| Hanie Tandorost1؛ Samira Abbasi1؛ Fatemeh Amiri2 | ||
| 1Biomedical engineering department, Hamedan university of technology, Hamedan, Iran. | ||
| 2ِComputer engineering department, Hamedan university of technology, Hamedan,, Iran. | ||
| چکیده [English] | ||
| The Iranian people were confronted with a range of emotions during the Covid-19 crisis, which they shared on social media platforms. Social media played a crucial role in disseminating information and reflecting public sentiment during the pandemic. Consequently, governments and health organizations worldwide recognized the importance of analyzing social media data. Many researchers have examined these data using different approaches worldwide. This study focuses on the polarity analysis and classification of messages posted on social media during the COVID-19 crisis. The study analyzed messages shared by Persian-language users on social networks using natural language processing and deep learning techniques. Various deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and fuzzy-LSTM were used to classify the data as positive or negative polarity. The three-layer deep convolutional neural network achieved the highest accuracy of 72.29%. Finally, a comprehensive comparison of the different networks used was conducted across multiple aspects. | ||
| کلیدواژهها [English] | ||
| Social media, Corona virus, Natural language processing (NLP), Deep learning, Polarity | ||
| مراجع | ||
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[1] L.-C. Chen, C.-M. Lee, and M.-Y. Chen, “Exploration of social media for sentiment analysis using deep learning,” Soft Computing, Vol. 24, NO. 11, 2020, pp. 8187-8197. [2] O. Ahlgren, "Research on sentiment analysis: the first decade." 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016, pp. 890-899. [3] D. Q. Nguyen, D. Q. Nguyen, T. Vu et al., “Sentiment classification on polarity reviews: an empirical study using rating-based features,” 2014. [4] S. Shumaly, M. Yazdinejad, and Y. Guo, “Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings,” PeerJ Computer Science, Vol. 7, 2021, pp. e422. [5] S. Wu, Y. Liu, J. Wang et al., “Sentiment analysis method based on Kmeans and online transfer learning,” Comput. Mater. Contin, Vol. 60, 2019, pp. 1207-1222. [6] D. Dangi, D. K. Dixit, and A. Bhagat, “Sentiment analysis of COVID-19 social media data through machine learning,” Multimedia Tools and Applications, vol. 81, no. 29, 2022, pp. 42261-42283. [7] L. Nemes, and A. Kiss, “Social media sentiment analysis based on COVID-19,” Journal of Information and Telecommunication, Vol. 5, NO. 1, 2021, pp. 1-15. [8] M. Arbane, R. Benlamri, Y. Brik et al., “Social media-based COVID-19 sentiment classification model using Bi-LSTM,” Expert Systems with Applications, Vol. 212, 2023, pp. 118710. [9] D. Xie, L. Zhang, and L. Bai, “Deep learning in visual computing and signal processing,” Applied Computational Intelligence and Soft Computing, Vol. 2017, 2017. [10] L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 8, NO. 4, 2018, pp. e1253. [11] J. P. R. Sharami, Sarabestani, P. A., & Mirroshandel, S. A. , “DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus.,” arXiv preprint 2020, arXiv:2004.05328. [12] ف. شمیم و د. سینا، " تحلیل احساسات در شبکه های اجتماعی با پردازش زبان طبیعی و رویکرد یادگیری عمیق" ، در ششمین کنفرانس ملی پژوهش های کاربردی در مهندسی کامپیوتر و فناوری اطلاعات، تهران، ایران، 24 بهمن، 1398. [13] H. Liang, X. Sun, Y. Sun et al., “Text feature extraction based on deep learning: a review,” EURASIP journal on wireless communications and networking, Vol. 2017, NO. 1, 2017, pp. 1-12. [14] D. W. Otter, J. R. Medina, and J. K. Kalita, “A survey of the usages of deep learning for natural language processing,” IEEE transactions on neural networks and learning systems, Vol. 32, NO. 2, 2020, pp. 604-624. [15] Y. Chen, “Convolutional neural network for sentence classification,” University of Waterloo, 2015. [16] W. K. Sari, D. P. Rini, and R. F. Malik, "Text classification using long short-term memory." 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), 2019, pp. 150-155. [17] R. Wang, Z. Li, J. Cao et al., "Convolutional recurrent neural networks for text classification." 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1-6. [18] Z. Shaheen, G. Wohlgenannt, and E. Filtz, “Large scale legal text classification using transformer models,” arXiv preprint, 2020, arXiv:2010.12871. [19] Z. Li, F. Liu, W. Yang et al., “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE transactions on neural networks and learning systems, 2021. [20] P. Sharma, and A. Singh, "Era of deep neural networks: A review." 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2017, pp. 1-5. [21] J. Xu, D. Chen, X. Qiu et al., “Cached long short-term memory neural networks for document-level sentiment classification,” arXiv preprint, 2016, arXiv:1610.04989. [22] K. Uma, and K. Meenakshisundaram, “Optimization based fuzzy deep learning classification for sentiment analysis,” Int J Sci Technol Res, Vol. 9, NO. 3, 2020, pp. 7. [23] B. Mohamed, H. Haytam, and F. Abdelhadi, "Applying Fuzzy Logic and Neural Network in Sentiment Analysis for Fake News Detection: Case of Covid-19," Combating Fake News with Computational Intelligence Techniques, 2022, pp. 387-400: Springer. [24] E. Ferri, and G. Langholz, "Neuro-Fuzzy Approach to Natural Language Understanding and Processing," Intelligent systems and interfaces, pp. 261-280: Springer, 2000. [25] X. Yu, C. Zhong, D. Li et al., "Sentiment analysis for news and social media in COVID-19." Proceedings of the 6th ACM SIGSPATIAL International Workshop on Emergency Management using GIS, 2020, pp. 1-4. [26] N. Chintalapudi, G. Battineni, and F. Amenta, “Sentimental analysis of COVID-19 tweets using deep learning models,” Infectious Disease Reports, Vol. 13, NO. 2, 2021, pp. 329-339. [27] T. Wang, K. Lu, K. P. Chow et al., “COVID-19 sensing: negative sentiment analysis on social media in China via BERT model,” Ieee Access, Vol. 8, 2020, pp. 138162-138169. [28] F. Amiri, S. Abbasi, and M. Babaie Mohamadeh, “Clustering Methods to Analyze Social Media Posts during Coronavirus Pandemic in Iran,” Journal of AI and Data Mining, 2022. [29] F. Kaveh-Yazdy, and S. Zarifzadeh, “Track Iran's national COVID-19 response committee’s major concerns using two-stage unsupervised topic modeling,” International journal of medical informatics, Vol. 145, 2021, pp. 104309. [30] P. Hosseini, P. Hosseini, and D. A. Broniatowski, “Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP,” arXiv preprint, 2020, arXiv:2005.08400. [31] Z. B. Nezhad, and M. A. Deihimi, “Twitter sentiment analysis from Iran about COVID 19 vaccine,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, Vol. 16, NO. 1, 2022, pp. 102367. [32] D.-H. Lee, "Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks." Workshop on challenges in representation learning, ICML, 2013, Vol. 3, NO. 2, p. 896. [33] F. Rodríguez-Torres, J. F. Martínez-Trinidad, and J. A. Carrasco-Ochoa, “An Oversampling Method for Class Imbalance Problems on Large Datasets,” Applied Sciences, Vol. 12, NO. 7, 2022, pp. 3424. [34] S. Jain, “Introduction to pseudo-labelling: A semi-supervised learning technique,” URL= https://www. analyticsvidhya.com/blog/2017/09/pseudo-labellingsemi-supervised-learning-technique, 2017. [35] P. Cascante-Bonilla, F. Tan, Y. Qi et al., "Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning." Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, NO. 8, 2021, pp. 6912-6920. [36] R. Mohammed, J. Rawashdeh, and M. Abdullah, "Machine learning with oversampling and undersampling techniques: overview study and experimental results." 2020 11th international conference on information and communication systems (ICICS), 2020, pp. 243-248. [37] A. Moreo, A. Esuli, and F. Sebastiani, "Distributional random oversampling for imbalanced text classification." Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016, pp. 805-808. [38] J. Kacprzyk, "Lecture notes in networks and systems," Springer, 2019. [39] N. Ketkar, "Introduction to keras," Deep learning with Python, pp. 97-111: Springer, 2017. [40] S.-H. Lee, K.-W. Huang, and C.-S. Yang, “TBAS: Token-based authorization service architecture in Internet of things scenarios,” International Journal of Distributed Sensor Networks, Vol. 13, NO. 7, 2017, pp. 1550147717718496. [41] H. Zhou, "Research of Text Classification Based on TF-IDF and CNN-LSTM." Journal of Physics: Conference Series, 2022, Vol. 2171, NO. 1, pp. 012021. [42] Y. Luan, and S. Lin, "Research on text classification based on CNN and LSTM." 2019 IEEE international conference on artificial intelligence and computer applications (ICAICA), 2019, pp. 352-355. [43] W. Yin, K. Kann, M. Yu et al., “Comparative study of CNN and RNN for natural language processing,” arXiv preprint, 2017, arXiv:1702.01923. [44] K. Yoon, “Convolutional Neural Networks for Sentence Classification [OL],” arXiv Preprint, 2014. [45] F. Ali, S. El-Sappagh, and D. Kwak, “Fuzzy ontology and LSTM-based text mining: a transportation network monitoring system for assisting travel,” Sensors, Vol. 19, NO. 2, 2019, pp. 234. [46] A. BERRAJAA, “Natural Language Processing for the Analysis Sentiment using a LSTM Model,” International Journal of Advanced Computer Science and Applications, Vol. 13, NO. 5, 2022. [47] K. FUKUSHIMA, “Neocognitron: Deep convolutional neural network,” Cognitive science, Vol. 29, NO. 1, 2022, pp. 14-23. [48] Y. Yu, X. Si, C. Hu et al., “A review of recurrent neural networks: LSTM cells and network architectures,” Neural computation, Vol. 31, NO. 7, 2019, pp. 1235-1270. [49] S. Sohangir, D. Wang, A. Pomeranets et al., “Big Data: Deep Learning for financial sentiment analysis,” Journal of Big Data, Vol. 5, NO. 1, 2018, pp. 1-25. | ||
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