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 proceedingConference contribution

    23 Citations (Scopus)
    376 Downloads (Pure)


    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 languageEnglish
    Title of host publicationProceedings of 2017 IEEE International Conference on Big Data
    Subtitle of host publication2017 International Workshop on Big Data Analytics for Cyber Intelligence and Defense
    Number of pages6
    ISBN (Electronic)9781538627150
    Publication statusPublished - 15 Jan 2018


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

    ASJC Scopus subject areas

    • Information Systems and Management
    • Control and Optimization
    • Information Systems
    • Hardware and Architecture
    • Computer Networks and Communications


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