Hybrid Deep Learning Models for Sentiment Analysis

Cach N Dang,María N Moreno-García,Fernando De La Prieta,Tao Jia

doi:10.1155/2021/9986920

Cach N Dang, María N Moreno-García + Show 2 more

Open Access

https://doi.org/10.1155/2021/9986920

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Abstract

Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets. Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. The hybrid models are compared against three single models, SVM, LSTM, and CNN. Both reliability and computation time were considered in the evaluation of each technique. The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM. The reliability of the latter was significantly higher.

Highlights

Sentiment analysis on information from social networks, such as Twitter or Facebook, is a research topic of growing interest today
We proposed the use of hybrid deep learning models for sentiment analysis from social network data
We tested the performance of mixing support vector machines (SVM), convolutional neural networks (CNN), and long short-term memory (LSTM), using two-word embedding techniques, Word2vec and BERT, on eight textual datasets of tweets and reviews

Summary

Introduction

Sentiment analysis on information from social networks, such as Twitter or Facebook, is a research topic of growing interest today. After reviewing some recent studies [1, 11, 12, 15, 17,18,19,20], we found that CNN and RNN are outperforming methods with a relatively high overall accuracy. Both shallow neural networks and deep neural networks are capable of approximating any function. When contrasted to shallow neural networks, deep neural networks have the advantage of being able to do the feature extraction in the process of learning on large datasets. Deep neural networks are able to create deep representations; at every layer, the network learns a new, more abstract representation of the input

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