Low Resource Domain Subjective Context Feature Extraction via Thematic Meta-learning
Keywords:
Data Analytics, Feature Extraction, Feedback review, Natural Language Processing, Text ProcessingAbstract
The volume of the data is directly proportional to the model's accuracy in data analytics for any particular domain. Once a developing field or discipline becomes apparent, the scarcity of the data volume becomes a challenging proponent for the correctness of a model and prediction. In the proposed state-of-the-art, a transitive empirical method has been used within the same contextual domain to extract features from a low-resource part via a heterogeneous field with factual data. Even though an example of text processing has been used for brevity, it is not limited. The success rate of the proposed model is 78.37%, considering model performance. But when considering human subject matter experts, the accuracy is 81.2%.
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Published
2023-08-03
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Articles
How to Cite
Agarwal, V., Goplani, A., Barai, M. K., Sarkar, A., & Sanyal, S. (2023). Low Resource Domain Subjective Context Feature Extraction via Thematic Meta-learning. International Journal of Electrical, Electronics and Computers, 8(3). https://journal-repository.com/index.php/ijeec/article/view/6526