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Multi-layered perceptron based deep learning model for emotion extraction on monolingual text using intelligence feature engineering and filtering techniques

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Abstract

Text Sentiment Analysis (TSA) for blogs on major microblogging platforms has grown drastically and is also very important as a field of research study. However, the paper focuses on the emotion in short text like Twitter which is exceedingly difficult due to the complexity of natural language and the informal structure employed in it, which has a restriction of 280 characters per tweet. In this proposed work, the combination of data filtering and feature engineering approaches are used to recognize the emotion in the short text. A Multi-Layered Perceptron-based Simplified Deep Learning Model (MLP-SDLM) is used in the proposed work to concatenates the filtering and feature engineering serially and parallelly. The third approach introduces the K-map based technique to combine the filtered and unfiltered textual and non-textual features efficiently. The results of proposed models are compared with traditional machine learning and deep learning classifiers and the performances of the proposed MLP-SDLM model gives 95.13% accuracy, K-map based technique produces 89.17% accuracy and MLP gives 88.7% significantly.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal, India

    Kumaran P

  2. Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India

    Rajeswari Sridhar & Hiran Nandy

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  1. Kumaran P
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  2. Rajeswari Sridhar
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  3. Hiran Nandy
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Correspondence to Kumaran P.

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Kumaran P, Sridhar, R. & Nandy, H. Multi-layered perceptron based deep learning model for emotion extraction on monolingual text using intelligence feature engineering and filtering techniques. Multimed Tools Appl 82, 44037–44052 (2023). https://doi.org/10.1007/s11042-023-15438-2

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