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 |
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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 |
Pages | 4630-4635 |
Number of pages | 6 |
ISBN (Electronic) | 9781538627150 |
DOIs | |
Publication status | Published - 15 Jan 2018 |
Event | 2017 IEEE International Conference on Big Data: 2017 International Workshop on Big Data Analytics for Cyber Intelligence and Defense - Westin Copley Place, Boston, Boston, United States Duration: 11 Dec 2017 → 14 Dec 2017 http://cci.drexel.edu/bigdata/bigdata2017/ |
Conference
Conference | 2017 IEEE International Conference on Big Data |
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Abbreviated title | Big Data 2017 |
Country/Territory | United States |
City | Boston |
Period | 11/12/17 → 14/12/17 |
Internet address |
Keywords
- sentiment analysis
- 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