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A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data

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Abstract

Social media sites are a popular medium for interaction in the modern, expanding globe, where everyone has a connection with social media in some way. The people are accustomed to reading reviews before making decisions, for instance reading comments for movies, eateries, online stores, and a variety of other products. Taking reviews entails being aware of what other people think. It can be described in one way as sentiment analysis or even as opinion mining. For correctly predicting sentiments from social corpus data, the turbulent flow optimized deep fused ensemble model is a novel and sophisticated approach for sentiment analysis. The preprocessed data were used to extract a variety of features, including bag of words, term frequency-inverse term frequency, word to vector, and Glove. Then, the contemporary turbulent flow of water-based optimization mechanism was used to select the features that would be most useful for training the classifier. In addition, the cutting-edge deep fused ensemble voting classifier is employed to construct a precise decision function in accordance with the average probability of the number of classifiers. This work uses the well-known benchmarking social corpus datasets from IMDB, Twitter, Airlines, Amazon, Crowded Flower, and Apple for system analysis and study.

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Data availability

Data sharing is not applicable to this article as social network-related datasets were generated or analyzed during the current study.

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Acknowledgements

I wish to acknowledge the help provided by developing this Research article “Centre for Networking and Cyber Defense” (CNCD)—Center for Excellence, School of Computing Science, Department of Information Technology, Hindustan Institute of Technology and Science, Kelambakkam, Tamil Nadu 603103, India.

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No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    E. Aarthi

  2. Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

    S. Jagan

  3. Department of Banking Technology, Pondicherry University, Puducherry, 605014, India

    C. Punitha Devi

  4. Department of Electronics and Communication, Saveetha Engineering College, Chennai, 602105, India

    J. Jeffin Gracewell

  5. Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, 501301, India

    Shruti Bhargava Choubey & Abhishek Choubey

  6. Department of Information Technology, Hindustan Institute of Technology and Science, Kelambakkam, Tamil Nadu, 603103, India

    S. Gopalakrishnan

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  1. E. Aarthi
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  2. S. Jagan
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  5. Shruti Bhargava Choubey
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Contributions

The author E.Aarthi involved in architectural design, implementation, and evaluation process presented in the paper. The author Jagan contributed and put effort on paper to organize the paper, developed the theoretical formalism, performed the analytic calculations, and performed the numerical simulations. C. Punitha Devi analyzed the data, technically contributed, and made English corrections and grammar checking. The author J.Jeffin Gracewell involved and helped to derive the mathematical equation. The Shruthi Bharghavav Choubey carried background study of the paper and helped the mathematical derivations. The author Abhishek Choubey involved and provided a factual review and helped edit the manuscript. The author S.Gopalakrishnan technically reviewed the overall manuscript and English corrections.

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Correspondence to S. Gopalakrishnan.

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Aarthi, E., Jagan, S., Devi, C.P. et al. A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data. Soc. Netw. Anal. Min. 14, 41 (2024). https://doi.org/10.1007/s13278-024-01203-2

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