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A supervised deep learning-based sentiment analysis by the implementation of Word2Vec and GloVe Embedding techniques - 2024

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

Research Area:  Machine Learning

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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Author(s) Name:  Pranati Rakshit & Avik Sarkar

Journal name:  Multimedia Tools and Applications

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Publisher name:  Springer

DOI:  10.1007/s11042-024-19045-7

Volume Information:  Volume 2,(2024)