Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation

Dabiah Ahmed Alboaneen; Huaglory Tianfield; Yan Zhang

doi:10.1109/BigData.2017.8258507

Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation

Dabiah Ahmed Alboaneen, Huaglory Tianfield, Yan Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

196 Downloads (Pure)

Abstract

In this paper, a new tweet analysing approach is proposed, which is composed of two main phases; feature selection and tweets classification. In the first phase, mutual information (MI) is used to select the best set of features to reduce the feature dimensions. In the second phase, a metaheuristic algorithm is used to optimise weights and biases of multi-layer perceptrons (MLPs) network and then implemented to classify twitter sentiments. Experimental results on existing twitter dataset show better performance of the glowworm swarm optimisation (GSO) based MLP over genetic algorithm (GA )and biogeography-based optimisation (BBO) algorithms.

Original language	English
Title of host publication	Proceedings of 2017 IEEE International Conference on Big Data
Subtitle of host publication	2017 International Workshop on Big Data Analytics for Cyber Intelligence and Defense
Number of pages	6
ISBN (Electronic)	9781538627150
DOIs	https://doi.org/10.1109/BigData.2017.8258507
Publication status	Published - 15 Jan 2018

Keywords

sentiment analysis
twitter
multi-layer perceptrons
glowworm swarm optimisation
genetic algorithm
biogeography-based optimisation

Documents and Links

10.1109/BigData.2017.8258507

Alboaneen, D.A. et al (2018) Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation
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Author accepted manuscript, 229 KB

Cite this

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abstract = "In this paper, a new tweet analysing approach is proposed, which is composed of two main phases; feature selection and tweets classification. In the first phase, mutual information (MI) is used to select the best set of features to reduce the feature dimensions. In the second phase, a metaheuristic algorithm is used to optimise weights and biases of multi-layer perceptrons (MLPs) network and then implemented to classify twitter sentiments. Experimental results on existing twitter dataset show better performance of the glowworm swarm optimisation (GSO) based MLP over genetic algorithm (GA )and biogeography-based optimisation (BBO) algorithms.",

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Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation. / Alboaneen, Dabiah Ahmed; Tianfield, Huaglory ; Zhang, Yan.

Proceedings of 2017 IEEE International Conference on Big Data: 2017 International Workshop on Big Data Analytics for Cyber Intelligence and Defense. 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Sentiment analysis via multi-layer perceptron trained by meta-heuristic optimisation

Abstract

Keywords

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