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Optimising agile social media analysis
Agile social media analysis involves building bespoke, one-off classification pipelines tailored to the analysis of specific datasets. In this study we investigate how the DUALIST architecture can be optimised for agile social media analysis. We evaluate several semi-supervised learning algorithms in conjunction with a Na ¨ive Bayes model, and show how these modifications can improve the performance of bespoke classifiers for a variety of tasks on a large range of datasets.
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Publication status
- Published
File Version
- Published version
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Page range
31-40Presentation Type
- paper
Event name
6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2015)Event location
LisbonEvent type
workshopEvent date
17th September 2015Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-06-06First Open Access (FOA) Date
2016-06-06First Compliant Deposit (FCD) Date
2016-06-06Usage metrics
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