Sentiment Analysis of Corona-Related Tweets in Iran Using Deep Neural Network

Basiri, Mohammad Ehsan; Habibi, Shirin; Nemati, Shahla

doi:10.22054/ims.2021.54705.1799

Document Type : Research Paper

Authors

¹ Assistant Professor, Department of Computer Engineering, Shahrekord University, Shahrekord, Iran(Corresponding Author: basiri@sku.ac.ir).

² Department of Computer Eng., Faculty of Engineering, Shahrekord University, Iran

³ Department of Computer Engineering, Faculty of Engineering, Shahrekord University, Iran

https://doi.org/10.22054/ims.2021.54705.1799

Abstract

With the spread of Covid-19 disease, quarantine, and social isolation, people are increasingly posting their opinions about the coronavirus on social networks such as Twitter. However, no study has yet been reported to analyze online opinions of individuals in order to understand their feelings about the Covid-19 epidemic in Iran. This study analyzes the emotions in the opinions of the Iranian people on the social network Twitter during the Corona crisis. For this purpose, a deep neural network model is presented. As there is no labeled dataset of Covid-19 tweets, the proposed model is first trained on the Stanford University Sentiment140 dataset, which contains 1.6 million tweets, and then used to classify the two classes of emotions contained in the collected corona-related tweets in Iran. The results show that the percentage of tweets with negative emotions is significantly higher than positive tweets. Also, the change in negative emotions of people in different months is proportional to the change in patient statistics.

Keywords

References

فخری، پروین و حسین زاده، مهدی. (1396). آنالیز محتوای کمپین‌های انتخاباتی 2016 ریاست جمهوری ایالات‌متحده آمریکا در توئیتر. فصلنامه مطالعات مدیریت کسب‌وکار هوشمند،5(20)، 156-121.

کوثری لنگری، روح‌الله؛ سردار، سهیلا؛ امین موسوی، سید عبدالله و رادفر، رضا. (1398). مدلی برای انتشار داده‌های شبکه‌های اجتماعی برخط با حفظ حریم خصوصی. مطالعات مدیریت کسب‌وکار هوشمند، 8(29)،112-86.

عباسی، فاطمه؛ سهرابی، بابک؛ مانیان، امیر و خدیور، آمنه. (1396). ارائه مدلی جهت دسته‏بندی احساسات خریداران کتاب با استفاده از رویکرد ترکیبی. مطالعات مدیریت کسب‌وکار هوشمند، 6(21)،92-65.

References

Abdar, M., Basiri, M. E., Yin, J., Habibnezhad, M., Chi, G., Nemati, S., & Asadi, S. (2020). Energy choices in Alaska: Mining people’s perception and attitudes from geotagged tweets. Renewable and Sustainable Energy Reviews, 124, 109781.

Basiri, M. E., Abdar, M., Cifci, M. A., Nemati, S., & Acharya, U. R. (2020). A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques. Knowledge-Based Systems, 105949.

Basiri, M. E., & Kabiri, A. (2017). Translation is not enough: comparing lexicon-based methods for sentiment analysis in Persian. In 2017 International Symposium on Computer Science and Software Engineering Conference (CSSE) IEEE. Shiraz: IEEE.

Basiri, M. E., & Kabiri, A. (2018). Words Are Important: Improving Sentiment Analysis in the Persian Language by Lexicon Refining. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 17(4), 26.

Cadena, A., Child, F., Craven, M., Ferrari, F., Fine, D., Franco, J., & Wilson, M. (2020). No Title.

Cambria, E. (2016). Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(3), 102–107.

Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017). A practical guide to sentiment analysis. Springer.

Chauhan, U. A., Afzal, M. T., Shahid, A., Abdar, M., Basiri, M. E., & Zhou, X. (2020). A comprehensive analysis of adverb types for mining user sentiments on amazon product reviews. World Wide Web.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint ArXiv,1810.04805.

FitzGerald, A., Kwiatkowski, K., Singer, V., & Smit, S. (2020). An instant economic crisis: How deep and how long. McKinsey Company, 6.

Fouladfar, F., Dehkordi, M. N., & Basiri, M. E. (2020). Predicting the Helpfulness Score of Product Reviews Using an Evidential Score Fusion Method. IEEE Access, 8, 82662–82687.

Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40(16), 6266–6282.

Haidong, S., Junsheng, C., Hongkai, J., Yu, Y., & Zhantao, W. (2020). Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing. Knowledge-Based Systems, 188, 105022.

Hassan, S.-U., Aljohani, N. R., Idrees, N., Sarwar, R., Nawaz, R., Mart\’\inez-Cámara, E., … Herrera, F. (2020). Predicting literature’s early impact with sentiment analysis in Twitter. Knowledge-Based Systems, 192, 105383.

Holshue, M. L., DeBolt, C., Lindquist, S., Lofy, K. H., Wiesman, J., Bruce, H., … others. (2020). First case of 2019 novel coronavirus in the United States. New England Journal of Medicine.

Illanes, P., Law, J., Mendy, A., Sanghvi, S., & Sarakatsannis, J. (2020). No Title.

Lin, Y.-H., Liu, C.-H., & Chiu, Y.-C. (2020). Google searches for the keywords of “wash hands” predict the speed of national spread of COVID-19 outbreak among 21 countries. Brain, Behavior, and Immunity.

Liu, B., & Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. In Mining Text Data, 415–463.

Merchant, R. M., & Lurie, N. (2020). Social media and emergency preparedness in response to novel coronavirus. Jama.

Nemati, S., Rohani, R., Basiri, M. E., Abdar, M., Yen, N. Y., & Makarenkov, V. (2019). A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition. IEEE Access, 7, 172948–172964.

Nowak, J., Taspinar, A., & Scherer, R. (2017). LSTM recurrent neural networks for short text and sentiment classification. International Conference on Artificial Intelligence and Soft Computing, 553–562.

Oueslati, O., Cambria, E., HajHmida, M. Ben, & Ounelli, H. (2020). A review of sentiment analysis research in Arabic language. Future Generation Computer Systems.

Perlman, S. (2020). Another decade, another coronavirus. Mass Medical Soc.

Pradhan, D., Biswasroy, P., Ghosh, G., Rath, G., & others. (2020). A review of current interventions for COVID-19 prevention. Archives of Medical Research.

Qiu, Y., Chen, X., & Shi, W. (2020). Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. Journal of Population Economics, 1.

Rill, S., Reinel, D., Scheidt, J., & Zicari, R. V. (2014). Politwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems, 69, 24–33.

Ruz, G. A., Henr\’\iquez, P. A., & Mascareño, A. (2020). Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106, 92–104.

Sharma, K., Seo, S., Meng, C., Rambhatla, S., & Liu, Y. (2020). Covid-19 on social media: Analyzing misinformation in twitter conversations. ArXiv Preprint ArXiv:2003.12309.

Velavan, T. P., & Meyer, C. G. (2020). The COVID-19 epidemic. Tropical Medicine & International Health, 25(3), 278.

Xu, Q., Bo, Z., Jiang, C., & Liu, Y. (2019). Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility. Knowledge-Based Systems, 166, 170–185.

Zhang, Z., Robinson, D., & Tepper, J. (2018). Detecting hate speech on twitter using a convolution-gru based deep neural network. European Semantic Web Conference, 745–760.

Fakhri, P., Hosseinzadeh Zadeh, M. (2017). Content Analysis of the 2016 United States Presidential Election Campaigns on Twitter. IT Management Studies, 5(20), 121-156. [InPersian]

Kosari Langari, R., Sardar, S., Amin Mousavi, S., & Radfar, R. (2019). A Model to Publish Online Social Networks Data with Privacy Preserving. IT Management Studies, 8(29), 87-112. [InPersian]

Abbasi, F., Sohrabi, B., Manian, A., & Khadivar, A. (2017). A Model to Classify Book Buyers’ Sentiments Using Ensemble Approach. IT Management Studies, 6(21), 65-92. [InPersian]

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‌ ‌استناد به این مقاله: بصیری، محمد احسان، حبیبی، شیرین، نعمتی، شهلا. (1400). تحلیل احساسات توئیت‌های مرتبط با کرونا در ایران با استفاده از شبکه عصبی عمیق، مطالعات مدیریت کسب وکار هوشمند، 10(37)، 109-134. DOI: 10.22054/IMS.2021.54705.1799

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Sentiment Analysis of Corona-Related Tweets in Iran Using Deep Neural Network

References

References

Volume 10, Issue 37September 2021Pages 109-134

Volume 10, Issue 37
September 2021
Pages 109-134