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Selective shallow models strength integration for emotion detection using GloVe and LSTM

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Abstract

Text analysis has gained immense popularity due to the widespread use of the internet and unrestricted access to people’s opinions provided by social media. Analyzing public emotions in real-time can enable us to predict problematic situations like civil unrest that may arise in the future allowing us to take measures to prevent or handle them. This paper proposes a novel technique for emotion detection that can be used in real-time due to its comparatively much smaller run time and smaller memory size. Present well-performing models for emotion detection are incapable of being used in real-time due to the incorporation of large deep learning models that make them considerably slower. This work proposes a technique to use multiple shallow models to surpass the performance of a single large model by selectively combining their strengths and disregarding their weaknesses. These shallow models work independently which allows them to be run in parallel to ensure a smaller execution time. This combined proposal achieved 86.16% accuracy in 00.98 milliseconds per input. Therefore, the experiments show that the proposed model outperforms state-of-the-art models. Moreover, the computational cost shows that the proposal may used for real time applications.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Aditya Vijayvergia

  2. National Institute of Technology Uttarakhand, Srinagar, Garhwal, India

    Krishan Kumar

Authors
  1. Aditya Vijayvergia
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  2. Krishan Kumar
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Correspondence to Krishan Kumar.

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Vijayvergia, A., Kumar, K. Selective shallow models strength integration for emotion detection using GloVe and LSTM. Multimed Tools Appl 80, 28349–28363 (2021). https://doi.org/10.1007/s11042-021-10997-8

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  • DOI: https://doi.org/10.1007/s11042-021-10997-8

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