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A supervised deep learning-based sentiment analysis by the implementation of Word2Vec and GloVe Embedding techniques

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Abstract

Sentiment analysis provides valuable insights into people’s opinions, emotions, and attitudes, enabling businesses to make more informed decisions, improve customer satisfaction, and stay competitive in today’s market. Now-a-days due to the accessibility of social networking platforms like Twitter, Instagram, Facebook, WeChat, etc a bulk of data is being generated within a very small span of time. These data inherit highly potential information hidden in them. Consequently, these data may be analyzed using powerful tools of deep learning techniques to achieve tremendous societal benefits. Keeping these views in mind, the authors have planned an extensive experimental study to undertake the task of sentiment analysis. In this present work, thirty-six different Word2Vec and GloVe Embedded deep learning models have been developed on the deep learning based architecture namely Multilayer Perceptron (MLP), Convolutional Neural Network(CNN), Long-and-Short-Term Memory Network (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). All the developed models are compared based on their accuracies, F1-scores, and other evaluation parameters. Several promising results have emerged from this study. It has been observed that the Word2Vec embedded Bi-directional GRU model yields the best result with an average F1-score of 0.84 in the case of a train-test ratio of 80:20. Finally this type of elaborated comparative and comprehensive study may be considered unique in its nature and supposed to be used to develop expert sentiment analyser.

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Availability of data and materials

The datasets and materials used/generated and/or analysed during the current study are available from the corresponding author on reasonable request.

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Author notes

  1. Pranati Rakshit and Avik Sarkar are contributed equally to this work.

Authors and Affiliations

  1. Computer Science & Engineering, JIS College of Engineering, Block A, Phase III, Kalyani, 741235, West Bengal, India

    Pranati Rakshit & Avik Sarkar

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  1. Pranati Rakshit
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Contributions

Pranati Rakshit and Avik Sarkar equally contributed in conceptualization and implementation of the present work. Avik Sarkar and Pranati Rakshit wrote the Manuscript text, Pranati Rakshit prepared the figures and Avik Sarkar designed the tables. Both the authors reviewed the manuscript.

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Correspondence to Pranati Rakshit.

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1.This is an original research work solely developed by the authors. 2.Author have clearly mentioned the source of the data used in this research work in Section 3.

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Rakshit, P., Sarkar, A. A supervised deep learning-based sentiment analysis by the implementation of Word2Vec and GloVe Embedding techniques. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19045-7

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  • DOI: https://doi.org/10.1007/s11042-024-19045-7

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