Categorized Directory

Main Menu

  • Home
  • Search directory
  • Web crawlers
  • Collect data
  • Indexation
  • Bankroll

Categorized Directory

Header Banner

Categorized Directory

  • Home
  • Search directory
  • Web crawlers
  • Collect data
  • Indexation
  • Bankroll
Collect data
Home›Collect data›Analysis of Spatiotemporal Sentiment Variation of India’s Geotagged COVID-19 Tweets Using a Hybrid Deep Learning Model

Analysis of Spatiotemporal Sentiment Variation of India’s Geotagged COVID-19 Tweets Using a Hybrid Deep Learning Model

By Ed Robertson
February 3, 2022
0
0
  • 1.

    Shahriari, S., Hossein Rashidi, T., Azad, A. & Vafaee, F. COVIDSpread: Real-time prediction of the spread of COVID-19 based on time-series modeling. F1000Res ten1110 (2021).

    Google Scholar article

  • 2.

    COVID-19 tracker. Indian COVID-19 tracker. https://www.covid19india.org (2020).

  • 3.

    Barkur, G., Vibha and Kamath, GB Nationwide Lockdown Sentiment Analysis Due to COVID 19 Outbreak: Evidence from India. Asian J Psychiatrist 51102089 (2020).

    Google Scholar article

  • 4.

    Lancet, T. India under COVID-19 lockdown. Lancet 3951315 (2020).

    Google Scholar article

  • 5.

    Basha, SM & Rajput, DS Roadmap to Implementing Parallel Aspect Level Sentiment Analysis. Multimedia. App Tools 7829463–29492 (2019).

    Google Scholar article

  • 6.

    Basha, SM & Rajput, DS A supervised aspect-level sentiment model to predict overall sentiment on tweeter materials. IJMSO 1333 (2018).

    Google Scholar article

  • seven.

    Xue, J. et al. Twitter discussions and emotions about the COVID-19 pandemic: machine learning approach. J.Med. Internet res. 22e20550 (2020).

    Google Scholar article

  • 8.

    Bollen, J., Pepe, A. & Mao, H. Modeling audience mood and emotion: Twitter sentiment and socioeconomic phenomena. arXiv:0911.1583 [cs] (2009).

  • 9.

    Alharbi, ASM & de Doncker, E. Twitter Sentiment Analysis with a Deep Neural Network: An Improved Approach Using User Behavioral Information. Conn. System Res. 5450–61 (2019).

    Google Scholar article

  • ten.

    Chen, Y., Yuan, J., You, Q. & Luo, J. Twitter Sentiment Analysis via Two-Way Emoji Integration and Attention-Based LSTM. In Proceedings of the 26th ACM International Conference on Multimedia 117–125 (Association for Computing Machinery, 2018). https://doi.org/10.1145/3240508.3240533.

  • 11.

    Koç, S. Ş, Özer, M., Toroslu, İH., Davulcu, H. & Jordan, J. Triadic co-clustering of users, issues, and sentiments in political tweets. expert system. Appl. 10079–94 (2018).

    Google Scholar article

  • 12.

    Liao, S., Wang, J., Yu, R., Sato, K. & Cheng, Z. CNN for situational understanding based on sentiment analysis of Twitter data. Process Compute. Science. 111376–381 (2017).

    Google Scholar article

  • 13.

    Kruspe, A., Häberle, M., Kuhn, I. & Zhu, XX Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic. arXiv:2008.12172 [cs, stat] (2020).

  • 14.

    Jahanbin, K. & Rahmanian, V. Using Twitter and Web News Mining to Predict the COVID-19 Outbreak. Asian Pac. J.Too much. Med. 13378–80 (2020).

    CAS Google Scholar Article

  • 15.

    Park, HW, Park, S. & Chong, M. Medical news conversations and frames on Twitter: Infodemiological study of COVID-19 in South Korea. J.Med. Internet res. 22e18897 (2020).

    Google Scholar article

  • 16.

    Trajkova, M. et al. Exploring casual COVID-19 data visualizations on Twitter: topics and challenges. Computer science seven35 (2020).

    Google Scholar article

  • 17.

    Alamoodi, AH et al. Sentiment analysis and its applications in the fight against COVID-19 and infectious diseases: a systematic review. expert system. Appl. 167114155. https://doi.org/10.1016/j.eswa.2020.114155 (2020)

    Google Scholar article

  • 18.

    Bisanzio, D., Kraemer, MUG, Brewer, T., Brownstein, JS, and Reithinger, R. Geotagged social media Twitter data to describe the geographic spread of SARS-CoV-2. J. Travel Med. 27(5). https://academic.oup.com/jtm/article/27/5/taaa120/5875518 (2020).

  • 19.

    Cuomo, RE, Purushothaman, V., Li, J., Cai, M. & Mackey, TK Subnational longitudinal and geospatial analysis of COVID-19 tweets. PLOS ONE 15e0241330 (2020).

    CAS Google Scholar Article

  • 20.

    Liu, Y. et al. Prediction of the next category of points of interest based on bidirectional GRU networks for healthcare. Int. J. Intel. System https://doi.org/10.1002/int.22710 (2021).

    Google Scholar article

  • 21.

    Hung, M. et al. COVID-19 Sentiment Social Network Analysis: Application of Artificial Intelligence. J.Med. Internet res. 22e22590 (2020).

    Google Scholar article

  • 22.

    Nemes, L. & Kiss, A. Social media sentiment analysis based on COVID-19. J.Inf. Telecommun. https://doi.org/10.1080/24751839.2020.1790793 (2020).

    Google Scholar article

  • 23.

    Lyu, X., Chen, Z., Wu, D. & Wang, W. Sentiment analysis on Chinese Weibo regarding COVID-19. In Natural language processing and Chinese computing (eds Zhu, X. et al.) 710–721 (Springer, New York, 2020). https://doi.org/10.1007/978-3-030-60450-9_56.

    Google Scholar Chapter

  • 24.

    Chakraborty, K. et al. Sentiment analysis of COVID-19 tweets by deep learning classifiers – A study to show how popularity affects accuracy in social media. Appl. Soft calculation. 97106754 (2020).

    Google Scholar article

  • 25.

    Mostafa, L. Egyptian students’ sentiment analysis using Word2vec during the coronavirus (Covid-19) pandemic. In Proceedings of the International Conference on Advanced Intelligent Systems and Computing 2020 (eds. Hassanien, AE, Slowik, A., Snášel, V., El-Deeb, H. & Tolba, FM) 195–203 (Springer, 2021). https://doi.org/10.1007/978-3-030-58669-0_18.

  • 26.

    Imran, AS, Daudpota, SM, Kastrati, Z. & Batra, R. Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on COVID-19-related tweets. IEEE Access 8181074–181090 (2020).

    Google Scholar article

  • 27.

    Kabir, Y. & Madria, S. CoronaVis: A real-time COVID-19 tweet data analyzer and data repository. ten.

  • 28.

    Minaee, S., Azimi, E. & Abdolrashidi, A. Deep-Sentiment: Sentiment Analysis Using a CNN and Bi-LSTM Model Ensemble. arXiv:1904.04206 [cs, stat] (2019).

  • 29.

    Yenter, A. & Verma, A. Deep CNN-LSTM with Combined Cores of Multiple Branches for IMDb Review Sentiment Analysis. In 2017 IEEE 8th Annual Computing, Electronics and Mobile Communication Conference (UEMCON) 540–546 (2017). https://doi.org/10.1109/UEMCON.2017.8249013.

  • 30.

    Wang, X., Jiang, W. & Luo, Z. Combination of convolutional and recurrent neural networks for sentiment analysis of short texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers 2428–2437 (COLING 2016 Organizing Committee, 2016).

  • 31.

    Wang, J., Yu, L.-C., Lai, KR, and Zhang, X. Dimensional sentiment analysis using a regional CNN-LSTM model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 225–230 (Association for Computational Linguistics, 2016). https://doi.org/10.18653/v1/P16-2037.

  • 32.

    Twitter. Filter tweets in real time. https://developer.twitter.com/en/docs/twitter-api/v1/tweets/filter-realtime/overview (2020).

  • 33.

    Burton, SH, Tanner, KW, Giraud-Carrier, CG, West, JH & Barnes, MD “Right time, right place” health communication on Twitter: value and accuracy of location information. J.Med. Internet res. 14(6), e156. https://academic.oup.com/jtm/article/27/5/taaa120/5875518 (2012).

  • 34.

    Bennett, NC, Millard, DE, and Martin, D. Assessing Twitter geocoding resolution. In Proceedings of the 10th ACM Web Science Conference 239–243 (Association for Computing Machinery, 2018). https://doi.org/10.1145/3201064.3201098.

  • 35.

    Qazi, U., Imran, M. & Ofli, F. GeoCoV19: A dataset of hundreds of millions of multilingual COVID-19 tweets with location information. arXiv:2005.11177 [cs] (2020).

  • 36.

    Patel, P., Patel, D. & Naik, C. Sentiment analysis on film review using deep learning RNN method. In Intelligent data engineering and analysis (eds Satapathy, SC et al.) 155–163 (Springer, New York, 2021). https://doi.org/10.1007/978-981-15-5679-1_15.

    Google Scholar Chapter

  • 37.

    Liu, Y. et al. A long-term memory-based model for greenhouse climate prediction. Int. J. Intel. System 37135-151 (2022).

    Google Scholar article

  • 38.

    Jang, B., Kim, M., Harerimana, G., Kang, S. & Kim, JW Bi-LSTM model for increasing text classification accuracy: combination of Word2 with CNN and attention mechanism. Appl. Science. ten5841 (2020).

    CAS Google Scholar Article

  • 39.

    Eckle, K. & Schmidt-Hieber, J. A comparison of deep networks with ReLU activation function and linear spline-like methods. Neural network. 110232-242 (2019).

    Google Scholar article

  • 40.

    Feeling140. Sentiment140—A Twitter sentiment analysis tool. http://help.sentiment140.com/home.

  • Related posts:

    1. Biden administration will not seek to join Open Skies treaty after 2020 release
    2. Emory creates a new institute for personalized medicine in brain health | Emory University
    3. New Barber Shop Instills Bulldog Name and Spirit: Olmsted Dates and Data
    4. CICSE asks schools to submit average grades for students in class 11, internal exam – The New Indian Express

    Categories

    • Bankroll
    • Collect data
    • Indexation
    • Search directory
    • Web crawlers

    Recent Posts

    • Live-Action TV Spider-Mans Who Didn’t Appear in No Way Home
    • Bennet bill would create federal definition of school shooting, direct incident data collection
    • The 10 Most In-Demand Entry-Level Remote Jobs Landing Right Now
    • Face-Scanner Clearview accepts the limits of the legal settlement | Economic news
    • Ex-minister embroiled in Hellenic row over staff cuts

    Archives

    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • Privacy Policy
    • Terms and Conditions