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#6285. Comparative study for machine learning classifier recommendation to predict political affiliation based on online reviews
| September 2026 | publication date |
| Proposal available till | 20-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Computer Networks and Communications; Human-Computer Interaction; Information Systems; Computer Vision and Pattern Recognition; Artificial Intelligence; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №6285.1   | Contract №6285.2 | Contract №6285.3 | Contract №6285.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
In the current era of social media, different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events, campaigns, and elections. The acquisition, analysis, and presentation of such content have received considerable attention from opinion-mining researchers. For this purpose, different supervised and unsupervised techniques have been used. However, they have produced less efficient results, which need to be improved by incorporating additional classifiers with the extended data sets. The authors investigate different supervised machine learning classifiers for classifying the political affiliations of users. For this purpose, a data set of political reviews is acquired from Twitter and annotated with different polarity classes. After pre-processing, different machine learning classifiers like K-nearest neighbor, na?ve Bayes, support vector machine, extreme gradient boosting, and others, are applied.
Keywords:
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Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
In the current era of social media, different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events, campaigns, and elections. The acquisition, analysis, and presentation of such content have received considerable attention from opinion-mining researchers. For this purpose, different supervised and unsupervised techniques have been used. However, they have produced less efficient results, which need to be improved by incorporating additional classifiers with the extended data sets. The authors investigate different supervised machine learning classifiers for classifying the political affiliations of users. For this purpose, a data set of political reviews is acquired from Twitter and annotated with different polarity classes. After pre-processing, different machine learning classifiers like K-nearest neighbor, na?ve Bayes, support vector machine, extreme gradient boosting, and others, are applied.
Keywords:
+
Contacts :
