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Experiments for both sentence-level categorization and review-level categorization have been performed.
Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes.
The process of sentiment polarity categorization is twofold: sentence-level categorization and review-level categorization.
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The experimental result is promising, both in terms of the sentence-level categorization and the review-level categorization.
In Phase 3: 1) Two sentiment polarity categorization experiments are respectively performed based on sentence level and review level; 2) Performance of three classification models are evaluated and compared based on their experimental results.
These categorization and review of the collected publications can serve as a knowledge base for other BIM educators to: (a) realize major issues involved in BIM education, (b) develop strategies to incorporate BIM into TESs, and (c) develop BIM tertiary education frameworks and curricula that can take global BIM education in TESs to the next level.
Discussion and future work is presented in section 'Review-level categorization'.
Specifically, more features will be extracted and grouped into feature vectors to improve review-level categorizations.
Figure 8 F1 scores of review-level categorization.
For the sentence-level (review-level) categorization, a feature vector is formed based on a sentence (review).
Training data for review-level categorization already have ground truth tags, which are the star-scaled ratings.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com