IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v11y2019i9p190-d263379.html
   My bibliography  Save this article

Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

Author

Listed:
  • Nasir Jamal

    (School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China)

  • Chen Xianqiao

    (School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, China)

  • Hamza Aldabbas

    (Prince Abdullah bin Ghazi Faculty of Information and Technology, Al-Balqa Applied University, Al Salt 19117, Jordan)

Abstract

Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions’ features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets’ data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into normalized forms. Finally, the TensorFlow based deep learning with Keras algorithm is proposed to predict high-quality features for emotions classification. The proposed methodology is analyzed on a dataset of 1,600,000 tweets collected from the website ‘kaggle’. Comparison is made of the proposed approach with other state of the art techniques on different training ratios. It is proved that the proposed approach outperformed among other techniques.

Suggested Citation

  • Nasir Jamal & Chen Xianqiao & Hamza Aldabbas, 2019. "Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data," Future Internet, MDPI, vol. 11(9), pages 1-13, September.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:9:p:190-:d:263379
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/11/9/190/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/11/9/190/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kaplan, Andreas M. & Haenlein, Michael, 2010. "Users of the world, unite! The challenges and opportunities of Social Media," Business Horizons, Elsevier, vol. 53(1), pages 59-68, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schmidt, Christoph G. & Wuttke, David A. & Heese, H. Sebastian & Wagner, Stephan M., 2023. "Antecedents of public reactions to supply chain glitches," International Journal of Production Economics, Elsevier, vol. 259(C).
    2. Mahan, Joseph E. & Seo, Won Jae & Jordan, Jeremy S. & Funk, Daniel, 2015. "Exploring the impact of social networking sites on running involvement, running behavior, and social life satisfaction," Sport Management Review, Elsevier, vol. 18(2), pages 182-192.
    3. Molina, Arturo & Fernández, Alejandra C. & Gómez, Mar & Aranda, Evangelina, 2017. "Differences in the city branding of European capitals based on online vs. offline sources of information," Tourism Management, Elsevier, vol. 58(C), pages 28-39.
    4. Hassan Danaeefard & Ali Farazmand & Akram Dastyari, 2023. "The Iranian Coronavirus Pandemic (COVID-9) Crisismanship: Understanding the Contributions of National Culture, Media, Technology and Economic System," Public Organization Review, Springer, vol. 23(4), pages 1661-1682, December.
    5. Richey, Michelle & Ravishankar, M.N., 2019. "The role of frames and cultural toolkits in establishing new connections for social media innovation," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 325-333.
    6. Fathey Mohammed & Nabil Hasan Al-Kumaim & Ahmed Ibrahim Alzahrani & Yousef Fazea, 2023. "The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19," IJERPH, MDPI, vol. 20(3), pages 1-16, January.
    7. Drummond, Conor & O'Toole, Thomas & McGrath, Helen, 2022. "Social Media resourcing of an entrepreneurial firm network: Collaborative mobilisation processes," Journal of Business Research, Elsevier, vol. 145(C), pages 171-187.
    8. Davide Giacomini & Paola Zola & Diego Paredi & Mario Mazzoleni, 2020. "Environmental disclosure and stakeholder engagement via social media: State of the art and potential in public utilities," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(4), pages 1552-1564, July.
    9. repec:zib:zbmbmj:v:2:y:2023:i:2:p:114-123 is not listed on IDEAS
    10. Domenico CONSOLI, 2017. "The Use of Social Media and E-Commerce: a Winning Strategy for Small Businesses," North Economic Review, Technical University of Cluj Napoca, Department of Economics and Physics, vol. 1(1), pages 109-119, October.
    11. Maximilian ROBU, 2013. "A New Classification Of Smes In The Digital Economy Context," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 1, pages 150-155, June.
    12. Schejter, Amit M. & Tirosh, Noam, 2015. "“Seek the meek, seek the just”: Social media and social justice," Telecommunications Policy, Elsevier, vol. 39(9), pages 796-803.
    13. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    14. Martín-Rojas, Rodrigo & Garrido-Moreno, Aurora & García-Morales, Víctor J., 2023. "Social media use, corporate entrepreneurship and organizational resilience: A recipe for SMEs success in a post-Covid scenario," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    15. Daas, Piet J.H. & Puts, Marco J.H., 2014. "Social media sentiment and consumer confidence," Statistics Paper Series 5, European Central Bank.
    16. Stefano Di Lauro & Aizhan Tursunbayeva & Gilda Antonelli & Marcello Martinez, 2021. "Organizational and Corporate Identity on Social Media: A Literature Review," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(4), pages 1-53, July.
    17. Vasile-Daniel Păvăloaia & Elena-Mădălina Teodor & Doina Fotache & Magdalena Danileţ, 2019. "Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    18. Zhepeng Li & Xiao Fang & Xue Bai & Olivia R. Liu Sheng, 2017. "Utility-Based Link Recommendation for Online Social Networks," Management Science, INFORMS, vol. 63(6), pages 1938-1952, June.
    19. Ying Guo & Xiantao Xiao, 2022. "Author-level altmetrics for the evaluation of Chinese scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 973-990, February.
    20. Irina Maiorescu & Mihaela Bucur & Bogdan Georgescu & Daniel Moise & Vasile Alecsandru Strat & Ion Daniel Zgură, 2020. "Social Media and IOT Wearables in Developing Marketing Strategies. Do SMEs Differ From Large Enterprises?," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    21. Shiwei Shen & Marios Sotiriadis & Qing Zhou, 2020. "Could Smart Tourists Be Sustainable and Responsible as Well? The Contribution of Social Networking Sites to Improving Their Sustainable and Responsible Behavior," Sustainability, MDPI, vol. 12(4), pages 1-21, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:11:y:2019:i:9:p:190-:d:263379. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.