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IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis

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Abstract

Sentiment analysis is a method used in machine learning to identify and examine the sentiments that are concealed in text. Annotated data is a requirement for sentiment analysis. This data is frequently manually annotated, which is a laborious, costly, and time-consuming procedure. In this work, a fully automated sentiment analysis annotation method has been devised to overcome these resource constraints. This work develops the clever and novel Inventive Optimized Deep Ensemble Augmented Learning (IDEAL) sentiment analysis system. Cleaning up the social data input is the first step in this data pretreatment process. This includes validation of missing numbers, spelling correction, noise reduction, and standardization. By implementing the Multi-Model Feature Extraction technique, the attributes Word to Vector, Glove, and Bag of Words are recovered from the social data. The ideal subset of features is then chosen using a novel, state-of-the-art technique called the Intelligent Mother Optimization technique (IMOA), which expedites the classifier's training and testing. Furthermore, the classification of attitudes into three categories—positive, negative, and neutral—is accomplished by a classifier model known as Hybrid Convoluted Bi-directional—Long Short Term Memory. The efficacy of the proposed IDEAL framework is evaluated by comparing it to the conventional sentiment prediction techniques and validating a variety of assessment metrics. The overall findings show that, with a 99% efficiency rate and high sentiment prediction accuracy of up to 99.2%, the suggested IDEAL framework performs better than the competition. This is primarily due to the inclusion of novel mining methodologies.

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Availability of data and materials

Data sharing not applicable to this article as Social Network related datasets were generated or analyzed during the current study.

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Funding

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

Author information

Authors and Affiliations

  1. JNIAS School of Planning and Architecture, Hyderabad, Telangana, 500034, India

    Aditya Mudigonda

  2. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Andhra Pradesh, 522302, India

    Usha Devi Yalavarthi

  3. Department of Electronics and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andra Pradesh, 520007, India

    P. Satyanarayana

  4. College of Technical Engineering, The Islamic University, Najaf, Iraq

    Ahmed Alkhayyat

  5. Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, Tamil Nadu, 600 123, India

    A. N. Arularasan

  6. Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Medchal, Hyderabad, Telangana, 501301, India

    S. Sankar Ganesh

  7. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

    CH. Mohan Sai Kumar

Authors
  1. Aditya Mudigonda
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  2. Usha Devi Yalavarthi
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  3. P. Satyanarayana
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  4. Ahmed Alkhayyat
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  5. A. N. Arularasan
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  6. S. Sankar Ganesh
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  7. CH. Mohan Sai Kumar
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Contributions

The author Mudigonda involved in architectural design, implementation and evaluation process presented in the paper. The author Yalavarthi contributed and put effort on paper to organize the Paper. Developed the theoretical formalism, performed the analytic calculations and performed the numerical simulations. P analyzed the data, technically contributed and made English Corrections and grammar checking. The author alkhayyat involved and helped to derive the mathematical equation. The A.N.carried background study of the Paper and helped the mathematical derivations. The author S involved and provided a factual review and helped edit the manuscript. The author CH. Mohan Sai Kumar technically review the Overall Manuscript and English Corrections and helping for Revision work.

Corresponding author

Correspondence to P. Satyanarayana.

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The authors declare no competing interests.

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All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki. I consent to participate in the research project and the following has been explained to me: the research may not be of direct benefit to me. My participation is completely voluntary. My right to withdraw from the study at any time without any implications to me.

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Mudigonda, A., Yalavarthi, U.D., Satyanarayana, P. et al. IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis. Soc. Netw. Anal. Min. 14, 89 (2024). https://doi.org/10.1007/s13278-024-01249-2

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  • DOI: https://doi.org/10.1007/s13278-024-01249-2

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