https://github.com/daniel-furman/nlp-dataset-mixing-experiments
Data-source mixing for social media caption classification
https://github.com/daniel-furman/nlp-dataset-mixing-experiments
Science Score: 13.0%
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Low similarity (4.6%) to scientific vocabulary
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Data-source mixing for social media caption classification
Basic Info
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README.md
NLP-experiments-social-media
Abstract
Domain shift often hinders the predictive performance of machine learning models where it counts most, on unseen data. However, social media datasets in NLP are in general inflexible to domain shift as they are commonly sourced solely from Twitter, which fails to capture the variation of natural language that exists across different social media platforms. Here, we examined the potential of multi-platform mixing for domain adaptation by combining Instagram captions in equal proportion to Tweets for two authorship analysis tasks. The resulting SVM and LR classifiers saw significant boosts in performance when compared to otherwise identical models constructed entirely from Tweets (6% average F1 increase), as measured in cross-domain testing on Facebook captions.
Background Figures
Figure 1: Bert embeddings EDA | Figure 2: Dataframe head
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Results
Figure 3: Modeling experimental results
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Owner
- Name: Daniel Furman
- Login: daniel-furman
- Kind: user
- Location: San Francisco
- Company: @twosixcapital
- Repositories: 6
- Profile: https://github.com/daniel-furman
Master’s student, UC Berkeley School of Information. University of Pennsylvania alum. DS @twosixcapital. Prev MLE @understory.ai.
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| Name | Commits | |
|---|---|---|
| Daniel Furman | d****n@g****m | 15 |
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