PARTICLE SWARM OPTIMIZATION (PSO) APPROACH FOR FEATURE SELECTION IN SENTIMENT ANALYSIS FOR PROPAGANDA ISSUE /
This research represents the implementation of the PSO (Particle Swarm Optimization) (feature selection) performance in the propaganda domain for sentiment analysis. The effectiveness of the PSO algorithm is tested during the datasets from Kaggle concerning Donald Trump and Hillary Clinton;s tweets...
Saved in:
| Main Author: | |
|---|---|
| Corporate Author: | |
| Format: | Thesis Book |
| Language: | English |
| Published: |
Kuala Lumpur :
Fakulti Pengajian Dan Pengurusan Pertahanan UPNM,
2022.
|
| Series: | Tesis
Thesis |
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research represents the implementation of the PSO (Particle Swarm Optimization) (feature selection) performance in the propaganda domain for sentiment analysis. The effectiveness of the PSO algorithm is tested during the datasets from Kaggle concerning Donald Trump and Hillary Clinton;s tweets during US Election Presidential in 2016. |
|---|---|
| Item Description: | This thesis accompanied by 1 CD ROM bearing the same call number and available at circulation counter |
| Physical Description: | xiv, 88 leaves : illustrations ; 30 cm. |
| Bibliography: | Includes bibliographical references |