Name
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 meta-heuristic 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BigData.2017.8258507 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | DocType | ISSN |
Sentiment analysis, Twitter, multi-layer perceptrons, glowworm swarm optimisation, genetic algorithm, biogeography-based optimisation | Conference | 2639-1589 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dabiah Ahmed Alboaneen | 1 | 7 | 1.84 |
| huaglory tianfield | 2 | 427 | 45.76 |
| yan zhang | 3 | 67 | 20.55 |