Sentiment Analysis on Large-Scale Covid-19 Tweets using Hybrid Convolutional LSTM Based on Naïve Bayes Sentiment Modeling
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Abstract
Millions of lives were affected rapidly throughout the world when the Covid-19 outbreak spread by leaps and bounds. During this catastrophic period, people used to express their condolence as well as emotions through different social networks. In order to analyze the public comments on Twitter, an experimental approach is developed based on popular words regarding this pandemic. In this paper, various NLP-based research works are discussed on sentiment analysis, trend prediction, topic modeling, learning mechanisms, etc. Furthermore, the hybrid deep learning models are developed based on the Naïve Bayes sentiment model to predict the sentiment from the collected huge number of Coronavirus-related tweets. After performing the n-gram analysis, the Covid-19 specific words are extracted based on their popularity. The public sentiment trend has been analyzed using the extracted topics related to Covid-19 and the tweets are classified according to their sentiment scores. The distinguished sentiment ratings are assigned to the collected tweets based on their sentiment class. Then Convo-Sequential and Convo-Bidirectional long-short term networks are trained using fine-grained sentiment-rated tweets to categorize Covid-19 tweets into five different sentiment classes. Finally, our proposed Convo-Sequential and Convo-Bidirectional LSTM models achieved 84.52% and 85.03% of validation accuracy respectively for the first phase dataset whereas using the second phase dataset the models obtained the validation accuracy of 86.58% and 87.22% respectively.
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References
S. Tuli, S. Tuli, R. Tuli and S. S. Gill, “Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing,” Internet of Things, vol. 11, 2020.
A. D. Dubey, “Twitter Sentiment Analysis during COVID-19 Outbreak,” 2021. [Online]. Available: SSRN, https://ssrn.com/abstract=3572023
S. Das and A. K. Kolya, “Sense GST: Text mining & sentiment analysis of GST tweets by Naive Bayes algorithm,” 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, India, pp. 239-244, 2017.
X. Hu, L. Tang, J. Tang and H. Liu, “Exploiting social relations for sentiment analysis in microblogging,” in Proceedings of the sixth ACM international conference on Web search and data mining (WSDM ’13), pp. 537-546, 2013.
H. Lee, Y. Han, K. Kim and K. Kim, “Sentiment analysis on online social network using probability Model,” in Proceedings of the Sixth International Conference on Advances in Future Internet, pp. 14-19, 2014.
D. Jurafsky, Speech and Language Processing, India:Pearson Education, 2000.
P. Fornacciari, M. Mordonini and M. Tomaiuolo, “Social network and sentiment analysis on Twitter: Towards a combined approach,” in KDWeb, pp. 53-64, 2015.
P. Zhou, Z. Qi, S. Zheng, J. Xu, H. Bao and B. Xu, “Text Classification Improved by Integrating Bidirectional LSTM with Twodimensional Max Pooling,” 2016. [Online]. Available: arXiv preprint arXiv:1611.06639, https://doi.org/10.48550/arXiv.1611.06639
C. Baecchi, T. Uricchio, M. Bertini, and A. Del Bimbo, “A multimodal feature learning approach for sentiment analysis of social network multimedia,” Multimedia Tools and Applications, vol. 75, no. 5, pp.2507-2525, 2016.
C. Baziotis, N. Pelekis and C. Doulkeridis, “DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis,” Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 747–754, 2017.
Y. Yang, and J. Eisenstein, “Overcoming language variation in sentiment analysis with social attention,” Transactions of the Association for Computational Linguistics, vol. 5, pp. 295-307, 2017.
B. Nakisa, M. N. Rastgoo, A. Rakotonirainy, F. Maire and V. Chandran, “Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework,” in IEEE Access, vol. 6, pp. 49325-49338, 2018.
L. Gong and H. Wang, “When sentiment analysis meets social network: A holistic user behavior modeling in opinionated data,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1455-1464, 2018.
B. Dahal, S. A. P. Kumar and Z. Li, “Spatiotemporal Topic Modeling and Sentiment Analysis of Global Climate Change Tweets,” Social Network Analysis and Mining, vol. 9, no. 1, pp.1-20, 2019.
N. C. Dang, M. N. Moreno-Garc ́ıa and F. De la Prieta, “Sentiment analysis based on deep learning: A comparative study,” Electronics, vol. 9, no. 3, pp. 483, 2020.
P. Chauhan, N. Sharma and G. Sikka, “The emergence of social media data and sentiment analysis in election prediction,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-27, 2020.
X. Ji, S. A. Chun and J. Geller, “Monitoring Public Health Concerns Using Twitter Sentiment Classifications,” 2013 IEEE International Conference on Healthcare Informatics, Philadelphia, PA, USA, pp. 335-344, 2013.
G. Coppersmith, M. Dredze and C. Harman, “Quantifying Mental Health Signals in Twitter,” Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Baltimore, Maryland, USA, pp. 51-60, 2014.
D. Georgiou, A. MacFarlane and T. RussellRose, “Extracting sentiment from healthcare survey data: An evaluation of sentiment analysis tools,” 2015 Science and Information Conference (SAI), London, UK, pp. 352-361, 2015.
D. L. Mowery, A. Park, C. Bryan and M. Conway, “Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health,” Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), Osaka, Japan, pp. 182-191, 2016.
Y. Zhang, M. Chen, D. Huang, D. Wu and Y. Li, “iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization,” Future Generation Computer Systems, vol. 66, pp.30-35, 2017.
A. Vij and J. Pruthi, “An automated psychometric analyzer based on sentiment analysis and emotion recognition for healthcare,” Procedia computer science, vol. 132, pp. 1184-1191, 2018.
J. Lee, J. Kim, Y. J. Hong, M. Piao, A. Byun, H. Song and H. S. Lee, “Health information technology trends in social media: using twitter data,” Healthcare informatics research, vol. 25, no. 2, p.99 , 2019.
L. Abualigah, H. E. Alfar, M. Shehab and A. M. A. Hussein, “Sentiment analysis in healthcare: a brief review,” Recent Advances in NLP: The Case of Arabic Language, pp.129-141, 2020.
P. Arora, H. Kumar and B. K. Panigrahi, “Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India,” Chaos, Solitons & Fractals, vol. 139, p.110017, 2020.
I. Arpaci, S. Alshehabi, M. Al-Emran, M. Khasawneh, I. Mahariq, T. Abdeljawad and A. E. Hassanien, “Analysis of twitter data using evolutionary clustering during the COVID-19 pandemic,” Computers, Materials & Continua, vol. 65, no. 1, pp.193-204, 2020.
S. Das and A. K. Kolya, “Predicting the Pandemic: Sentiment Evaluation and Predictive Analysis of Large-Scale Tweets on Covid-19 by Deep Convolutional Neural Network,” Evolutionary Intelligence, vol. 15, pp. 1913-1934, 2022.
Md. Y. Kabir and S. Madria, “CoronaVis: A Real-time COVID-19 Tweets Data Analyzer and Data Repository,” 2020. [Online]. Available: arXiv preprint arXiv:2004.13932, https://doi.org/10.48550/arXiv.2004.13932
J. Samuel, G. G. Ali, M. Rahman, E. Esawi, and Y. Samuel, “Covid-19 public sentiment insights and machine learning for tweets classification,” Information, vol. 11, no. 6, p.314, 2020.
H. Jelodar, Y. Wang, R. Orji and S. Huang, “Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: Nlp using lstm recurrent neural network approach,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp.27332742, 2020.
V. K. R. Chimmula and L. Zhang, “Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos,” Solitons & Fractals, vol. 135, p.109864, 2020.
N. Zheng et al., “Predicting COVID-19 in China Using Hybrid AI Model,” in IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 2891-2904, July 2020.
K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Applied Soft Computing, vol. 97, p.106754, 2020.
M. A. Al-Shaher, “A hybrid deep learning and NLP based system to predict the spread of Covid-19 and unexpected side effects on people,” Periodicals of Engineering and Natural Sciences (PEN), vol. 8, no. 4, pp.2232-2241, 2020.
S. Behl, A. Rao, S. Aggarwal, S. Chadha and H. S. Pannu, “Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises,” International journal of disaster risk reduction, vol. 55, p.102101, 2021.
Q. Yang et al., “SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic,” 2020. [Online]. Available: arXiv preprint arXiv:2006.10842, https://doi.org/10.48550/arXiv.2006.10842
M. M. Rahman and M. N. Islam, “Exploring the performance of ensemble machine learning classifiers for sentiment analysis of COVID-19 tweets,” in Sentimental Analysis and Deep Learning: Proceedings of ICSADL 2021, Singapore: Springer Singapore, pp. 383-396, 2021.
A. Timmaraju and V. Khanna, “Sentiment analysis on movie reviews using recursive and recurrent neural network architectures,” Semantic Scholar, pp. 1-5, 2015.
A. Yenter and A. Verma, “Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis,” 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, USA, pp. 540-546, 2017.
Z. Shaukat, A. A. Zulfiqar, C. Xiao, M. Azeem and T. Mahmood, “Sentiment analysis on IMDB using lexicon and neural networks,” SN Applied Sciences, vol. 2, no. 2, pp. 1-10, 2020.
N. M. Ali, M. M. Abd El Hamid and A. Youssif, “Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models,” International Journal of Data Mining & Knowledge Management Process (IJDKP), vol.9, no.2/3, May 2019.
J. Camacho-Collados and M. T. Pilehvar, “On the role of text preprocessing in neural network architectures: An evaluation study on text categorization and sentiment analysis,” 2018. [Online]. Available: arXiv preprint arXiv:1707.01780, https://doi.org/10.48550/arXiv.1707.01780
S. Minaee, E. Azimi and A. Abdolrashidi, “Deepsentiment: Sentiment analysis using ensemble of cnn and bi-lstm models,” 2019. [Online]. Available:arXiv preprint arXiv:1904.04206, https://doi.org/10.48550/arXiv.1904.04206
A. M. Dai and Q. V. Le, “Semi-supervised sequence learning,” 2015. [Online]. Available:arXiv preprint arXiv:1511.01432, https://doi.org/10.48550/arXiv.1511.01432
L. Guner, E. Coyne and J. Smit, “Sentiment analysis for Amazon,” com reviews, 2019.
X.L. Dong and G. De Melo, “A helping hand: Transfer learning for deep sentiment analysis,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1, Melbourne, Australia, pp. 2524-2534, 2018.