An efficient sentiment analysis using topic model based optimized recurrent neural network

Pathik, Nikhlesh; Shukla, Pragya

doi:10.21307/ijssis-2021-011

Abstract

In recent years, topic modeling and deep neural network-based methods have attracted much attention in sentiment analysis of online reviews. This paper presents a hybrid topic model-based approach for aspect extraction and sentiment classification of textual reviews. Latent Dirichlet allocation applied for aspect extraction and two-layer bi-directional long short-term memory (LSTM) for sentiment classification. This work also proposes a hill climbing-based approach for tunning model hyperparameters. The proposed model evaluated on three different datasets. Compared to the single-layer Bi-LSTM model, the proposed model gives 95, 95, and 86% accuracy for the movie, mobile, and hotel domain, respectively.

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An efficient sentiment analysis using topic model based optimized recurrent neural network

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