LSTM based models stability in the context of Sentiment Analysis for social media

• URL: http://arxiv.org/abs/2211.11246v1
• Date: Mon, 21 Nov 2022 08:31:30 GMT
• Title: LSTM based models stability in the context of Sentiment Analysis for social media
• Authors: Bousselham El Haddaoui, Raddouane Chiheb, Rdouan Faizi and Abdellatif El Afia
• Abstract summary: We present various LSTM models and their key parameters. We perform experiments to test the stability of these models in the context of Sentiment Analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning techniques have proven their effectiveness for Sentiment Analysis (SA) related tasks. Recurrent neural networks (RNN), especially Long Short-Term Memory (LSTM) and Bidirectional LSTM, have become a reference for building accurate predictive models. However, the models complexity and the number of hyperparameters to configure raises several questions related to their stability. In this paper, we present various LSTM models and their key parameters, and we perform experiments to test the stability of these models in the context of Sentiment Analysis.

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